{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Yazidi\Dictator Iraq\JCR\JCR Replication Instructions\JCR Legacies Replication log file.do
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 9 Jan 2025, 11:28:33

{com}. do "C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Yazidi\Dictator Iraq\JCR\JCR Replication Instructions\JCR Legacies Replication do file.do"
{txt}
{com}. *Legacies of Past and Present Violence: Evidence from Mosul, Iraq
. 
. *Replication Instructions
. 
. *Sam Whitt, Vera Mironova, Douglas Page
. 
. *Below are instructions for replicating all manuscript and online appendix tables and figures in STATA using the dataset "JCR Legagies Replication data.dta". Please contact Sam Whitt (swhitt@highpoint.edu) for questions regarding data replication. See also the dofile "JCR Legagies Replication do file". 
. 
. *Note: You may need to install STATA packages for the cibar, cem, and iebaltab commands. Use findit with the command name to identify and download the appropriate packets to install. 
. 
. *Note: In addition, some graphs require additional formatting using filename.grec files with the graph play command. To format a graph, simply run the command to generate the graph in the do file in STATA, then open the "Graph Editor" in STATA and click on the GREEN "Play Recording" button, then select "Browse" to select the grec file from the folder "grec files for STATA graph formatting" among Replication files. The name of the grec file is indicated in the note below the graph command in the do file for the specific graph you wish to format. This should automatically format the graph, which you may then save to a location of your choosing.
. 
. *"Stata user generated commands to install for replication purposes"
. 
. *"cibar"
. 
. ssc install cibar, replace
{txt}checking {hilite:cibar} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"iebaltab from ietoolkit"
. 
. ssc install ietoolkit, replace
{txt}checking {hilite:ietoolkit} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *cem
. 
. ssc install cem, replace
{txt}checking {hilite:cem} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"catcibar"
. net install catcibar, from("https://aarondwolf.github.io/catcibar") replace
checking {hilite:catcibar} consistency and verifying not already installed...
all files already exist and are up to date.
{txt}
{com}. 
. *powerlog
. net install powerlog, from(https://stats.oarc.ucla.edu/stat/stata/ado/analysis) replace
checking {hilite:powerlog} consistency and verifying not already installed...
all files already exist and are up to date.
{txt}
{com}. 
. *Manuscript Replication
. 
. *In text replication 
. 
. *Before ISIS, 74% of our Mosul sample had experienced at least one previous form of victimization resulting in death or injury of family members. However, few (<1%) suffered more than one episode of kinship-related violence before ISIS, and incidents of victimization before the arrival to ISIS are not well correlated with one another. 
. 
. tab addpreisisvictimall

   {txt}additive {c |}
   pre-isis {c |}
     victim {c |}
      index {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         70       25.18       25.18
{txt}          1 {c |}{res}        206       74.10       99.28
{txt}          2 {c |}{res}          2        0.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. 
. *We note high levels of abuse directed at women (39%) and looting of property (20%) by ISIS.  Overall, everyone in the Mosul sample experienced at least one form of victimization, while 70% experienced two forms, 5% three forms, but few (<2%) indicated more than three. 
. 
. tab womenabusedlib

   {txt}Women or {c |}
      other {c |}
     family {c |}
    members {c |}
  abused or {c |}
 assaulted? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        170       61.15       61.15
{txt}          1 {c |}{res}        108       38.85      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. tab homelootedlib

    {txt}Home or {c |}
   property {c |}
    looted? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        222       79.86       79.86
{txt}          1 {c |}{res}         56       20.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. tab addisisvictimall

   {txt}additive {c |}
isis victim {c |}
      index {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         66       23.74       23.74
{txt}          2 {c |}{res}        194       69.78       93.53
{txt}          3 {c |}{res}         14        5.04       98.56
{txt}          4 {c |}{res}          2        0.72       99.28
{txt}          5 {c |}{res}          2        0.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. 
. *We found that everyone in the sample was either aware of ISIS killing Iraqi Sunni (79%) or saw ISIS killing Sunni Iraqis (21%) directly, but surprisingly few indicated that they either heard or witnessed ISIS killing any of the mentioned out-groups (8% and 2% respectively). 
. 
. tab heardkillsunni

   {txt}heard of {c |}
       ISIS {c |}
    killing {c |}
      Sunni {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         58       20.86       20.86
{txt}          1 {c |}{res}        220       79.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. tab sawkillsunni

   {txt}saw ISIS {c |}
    killing {c |}
      Sunni {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        220       79.14       79.14
{txt}          1 {c |}{res}         58       20.86      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. tab dheardkilloutgroup

      {txt}dummy {c |}
   variable {c |}
        for {c |}
  awareness {c |}
of outgroup {c |}
    killing {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        256       92.09       92.09
{txt}          1 {c |}{res}         22        7.91      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. tab dsawkilloutgroup

      {txt}dummy {c |}
   variable {c |}
        for {c |}
 witnessing {c |}
   outgroup {c |}
    killing {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        272       97.84       97.84
{txt}          1 {c |}{res}          6        2.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. 
. *With 99% of our sample identifying as religious Sunni and 97% as Arabs, we treat giving to Sunni victims' families as in-group altruism and allocations to families of other groups as out-group altruism. 
. 
. tab religion if mosul==1

    {txt}What is {c |}
       your {c |}
   religion {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Sunni {c |}{res}        274       98.56       98.56
{txt}       Shia {c |}{res}          2        0.72       99.28
{txt}  Christian {c |}{res}          2        0.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. tab ethnicity if mosul==1

    {txt}What is {c |}
       your {c |}
  ethnicity {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Arab {c |}{res}        269       96.76       96.76
{txt}       Kurd {c |}{res}          7        2.52       99.28
{txt}    Turkmen {c |}{res}          2        0.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. 
. *In Figure 3, we show that respondents allocated on average three-fourths (75%) of the endowment to a Sunni victim's family, followed by 15% of the endowment to Yazidi, 4% to Iraqi Shia, 4% to Kurdish and 1% to Christian ISIS victims. The average allotment to all non-Sunni Iraqi groups was 25.1% (St.Dev = 24.5%). Nothing was allocated to families of either foreign victims of ISIS or of people who identified as LGBT+. 
. 
. sum perdgsunni perdgshia perdgyazidi perdgkurd perdgchristian perdgforeign perdggay if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}perdgsunni {c |}{res}        278    74.67626    24.60305          0        100
{txt}{space 3}perdgshia {c |}{res}        278    3.884892    9.221368          0         50
{txt}{space 1}perdgyazidi {c |}{res}        278    15.39568    17.20617          0         80
{txt}{space 3}perdgkurd {c |}{res}        278    3.597122    9.614009          0         60
{txt}perdgchris~n {c |}{res}        278    .8633094    3.982274          0         30
{txt}{hline 13}{c +}{hline 57}
perdgforeign {c |}{res}        278           0           0          0          0
{txt}{space 4}perdggay {c |}{res}        278           0           0          0          0
{txt}
{com}. sum perdgoutgroup if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
perdgoutgr~p {c |}{res}        276    25.07246    24.48365          0        100
{txt}
{com}. 
. *Overall, respondents exhibit altruism consistent with in-group bias where 76% allocated more than half the allotment to the Sunni victims, including 28% who allocated all funding to the Sunni and nothing to any other groups. Only 24% allocated half or more of the endowment to out-group victims.
. 
. tab perdgsunni if mosul==1

    {txt}Percent {c |}
(0-100%) to {c |}
Iraqi Sunni {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}          2        0.72        0.72
{txt}         10 {c |}{res}          4        1.44        2.16
{txt}         20 {c |}{res}          7        2.52        4.68
{txt}         30 {c |}{res}          6        2.16        6.83
{txt}         40 {c |}{res}         27        9.71       16.55
{txt}         50 {c |}{res}         20        7.19       23.74
{txt}         60 {c |}{res}         11        3.96       27.70
{txt}         70 {c |}{res}         29       10.43       38.13
{txt}         80 {c |}{res}         63       22.66       60.79
{txt}         85 {c |}{res}          2        0.72       61.51
{txt}         90 {c |}{res}         28       10.07       71.58
{txt}        100 {c |}{res}         79       28.42      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. *(Note: subtract cum percent at 50% from 100-23.74=76.26)
. sum revdgsunnibias if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revdgsunni~s {c |}{res}        278    .2374101     .426263          0          1
{txt}
{com}. 
. *Footnote19: Only 16 respondents acknowledged being sexually assaulted by ISIS (14 of which were men), and sexual assault is likely underreported compared to other forms of victimization due to social stigma (see Koos 2017).
. 
. tab dsexassault if mosul==1

      {txt}dummy {c |}
   variable {c |}
 for sexual {c |}
    assault {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        262       94.24       94.24
{txt}          1 {c |}{res}         16        5.76      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. 
. *The average closeness ratings on the 0-10 scale ranged from strong feelings of closeness for Sunni (mean =  9.8) to lowest closeness for LGBT+ people (mean = 0.09). We generated an out-group empathy index based on the interim covariance of feelings toward all out-groups (mean 3.2, st. dev. 1.6, see online appendix for further details). 
. 
. sum closesunni closegay if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}closesunni {c |}{res}        278    9.820144    .6663852          6         10
{txt}{space 4}closegay {c |}{res}        276    .0942029    .3792372          0          3
{txt}
{com}. sum closeoutgroup if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
closeoutgr~p {c |}{res}        278    3.221583    1.581169          0        7.5
{txt}
{com}. 
. *In Table 3, we find stronger feelings of closeness to out-groups in the treatment group under victimization priming than in the control group, and the effect size is quite large (Cohen's d=0.76)
. 
. esize twosample closeoutgroup if mosul==1, by(victimorder) unequal

{txt}Effect size based on mean comparison, unequal variances

                               Obs per group:
                              victimorder==0 =         82
                              victimorder==1 =        196
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.7613053{col 34}{space 3}-1.034939{col 46}{space 3}-.4848896
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.7592344{col 34}{space 3}-1.032123{col 46}{space 3}-.4835706
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
            Satterthwaite's degrees of freedom ={col 51}{res}120.8535
{txt}
{com}. 
. *Manuscript Tables and Figures
. 
. *Figure 1. Past and Present Victimization (Self-Reported)
. 
. *See excel file for formatting. In Stata, use the following:
. 
. sum iraniraq-violence14 if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}iraniraq {c |}{res}        278    .1510791    .3587719          0          1
{txt}{space 5}gulfwar {c |}{res}        278    .0179856    .1331386          0          1
{txt}{space 1}saddampre90 {c |}{res}        278    .0359712    .1865542          0          1
{txt}saddampost90 {c |}{res}        278           0           0          0          0
{txt}{space 5}iraqwar {c |}{res}        278    .0323741    .1773108          0          1
{txt}{hline 13}{c +}{hline 57}
insurgency03 {c |}{res}        278     .205036     .404456          0          1
{txt}{space 5}crime03 {c |}{res}        278    .3129496    .4645303          0          1
{txt}{space 2}violence14 {c |}{res}        278    .2517986    .4348289          0          1
{txt}
{com}. graph bar iraniraq- violence14 if mosul==1,  blabel(bar, format(%9.3f)) legend(cols(1) position(3))
{res}{txt}
{com}. 
. *Figure 2. ISIS-Related Victimization (Self-Reported)
. 
. *See excel file for formatting. In Stata, use the following:
. 
. *Figure 2a.
. 
. sum punishedisis imprisonedisis injuredisis dsexassault fampunishedisis faminjuredisis famkilledisis womenabusedisis fleehomeisis homedamagedisis if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
punishedisis {c |}{res}        278    .1834532    .3877357          0          1
{txt}imprisoned~s {c |}{res}        278    .0143885    .1193007          0          1
{txt}{space 1}injuredisis {c |}{res}        278    .0143885    .1193007          0          1
{txt}{space 1}dsexassault {c |}{res}        278     .057554    .2333181          0          1
{txt}fampunishe~s {c |}{res}        278    .0431655     .203596          0          1
{txt}{hline 13}{c +}{hline 57}
faminjured~s {c |}{res}        278    .0683453    .2527926          0          1
{txt}famkilledi~s {c |}{res}        278    .0179856    .1331386          0          1
{txt}womenabuse~s {c |}{res}        278    .3884892    .4882858          0          1
{txt}fleehomeisis {c |}{res}        278    .0971223    .2966583          0          1
{txt}homedamage~s {c |}{res}        278    .1978417    .3990906          0          1
{txt}
{com}. 
. graph bar punishedisis imprisonedisis injuredisis dsexassault fampunishedisis faminjuredisis famkilledisis womenabusedisis fleehomeisis homedamagedisis ,  blabel(bar, format(%4.2f)) legend(cols(1) position(3))
{res}{txt}
{com}. 
. *Figure 2b.
. 
. sum imprisonedlib injuredlib faminjuredlib famkilledlib womenabusedlib homedamagedlib homelootedlib fledhomelib if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
imprisoned~b {c |}{res}        278    .1402878    .3479116          0          1
{txt}{space 2}injuredlib {c |}{res}        278     .028777    .1674806          0          1
{txt}faminjured~b {c |}{res}        278    .0647482    .2465248          0          1
{txt}famkilledlib {c |}{res}        278    .0143885    .1193007          0          1
{txt}womenabuse~b {c |}{res}        278    .3884892    .4882858          0          1
{txt}{hline 13}{c +}{hline 57}
homedamage~b {c |}{res}        278    .0503597    .2190805          0          1
{txt}homelooted~b {c |}{res}        278    .2014388    .4017984          0          1
{txt}{space 1}fledhomelib {c |}{res}        278    .0791367    .2704388          0          1
{txt}
{com}. 
. graph bar imprisonedlib injuredlib faminjuredlib famkilledlib womenabusedlib homedamagedlib homelootedlib fledhomelib ,  blabel(bar, format(%4.2f)) legend(cols(1) position(3))
{res}{txt}
{com}. 
. *Figure 3 Dictator Game Allocations 
. 
. graph bar perdgsunni perdgshia perdgyazidi perdgkurd perdgchristian perdgforeign perdggay if mosul==1,  blabel(bar, format(%9.1f))
{res}{txt}
{com}. graph save g1
{res}{txt}file {bf:g1.gph} saved

{com}. catcibar closesunni-closegay if mosul==1
{txt}
{com}. graph save g2
{res}{txt}file {bf:g2.gph} saved

{com}. graph combine "g1.gph" "g2.gph"
{res}{txt}
{com}. 
. *additional formatting required using the file "Dictator Game Allocations.grec" file. 
. 
. *Figure 4. Average Treatment Effects on Empathy
. 
. cibar closeoutgroup if mosul==1, over1(victimorder)
{res}{txt}
{com}. graph save g3
{res}{txt}file {bf:g3.gph} saved

{com}. 
. *Code for Jitter data (from Coppock 2021) in READ ME file.
. *Code to generate main dependent variable in READ ME file. (already generated)
. *Code to generate tetrachoric ISIS victim index in READ ME file. (already generated)
. *Code to generate tetrachoric Pre-ISIS victim index for Mosul in READ ME file. (already generated)
. *Code to generate variables related to witnessing or hearing about in-group/out-group violence in READ ME file. (already generated)
. 
. *Table 2. Victimization and Out-group Altruism (Logit Regression)
. 
. logit revdgsunnibias mmx_tetraisisvictim, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.01365}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-144.88816}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-144.88814}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-144.88814}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:14.88}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-144.88814}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0491}

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        revdgsunnibias{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_tetraisisvictimall {c |}{col 24}{res}{space 2} 3.426901{col 36}{space 2} .8883075{col 47}{space 1}    3.86{col 56}{space 3}0.000{col 64}{space 4}  1.68585{col 77}{space 3} 5.167952
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-2.424147{col 36}{space 2}  .374987{col 47}{space 1}   -6.46{col 56}{space 3}0.000{col 64}{space 4}-3.159108{col 77}{space 3}-1.689186
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_tetrapreisisvictim, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-147.92127}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-147.84262}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-147.84261}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:9.44}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0021}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-147.84261}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0297}

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}           revdgsunnibias{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_tetrapreisisvictimall {c |}{col 27}{res}{space 2} 1.056339{col 39}{space 2} .3438998{col 50}{space 1}    3.07{col 59}{space 3}0.002{col 67}{space 4} .3823079{col 80}{space 3}  1.73037
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}-1.527548{col 39}{space 2} .1927679{col 50}{space 1}   -7.92{col 59}{space 3}0.000{col 67}{space 4}-1.905366{col 80}{space 3}-1.149729
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias dsaworheardkilloutgroup if mosul==1, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.52069}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.05067}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.04879}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.04879}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:14.61}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.04879}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0480}

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         revdgsunnibias{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      z{col 57}   P>|z|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dsaworheardkilloutgroup {c |}{col 25}{res}{space 2} 1.693496{col 37}{space 2} .4430756{col 48}{space 1}    3.82{col 57}{space 3}0.000{col 65}{space 4}  .825084{col 78}{space 3} 2.561908
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}-1.357024{col 37}{space 2} .1557837{col 48}{space 1}   -8.71{col 57}{space 3}0.000{col 65}{space 4}-1.662354{col 78}{space 3}-1.051694
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_tetraisisvictim mmx_tetrapreisisvictim dsaworheardkilloutgroup hadcovid diedcovid female age professional laborer unemployed westmosul  if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-129.54463}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-128.17311}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-128.16604}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-128.16604}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-128.16604}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1588}

{txt}{ralign 91:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}           revdgsunnibias{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}mmx_tetraisisvictimall {c |}{col 27}{res}{space 2} 3.145674{col 39}{space 2} .4658875{col 50}{space 1}    6.75{col 59}{space 3}0.000{col 67}{space 4} 2.232551{col 80}{space 3} 4.058797
{txt}mmx_tetrapreisisvictimall {c |}{col 27}{res}{space 2} 1.410603{col 39}{space 2} .1426453{col 50}{space 1}    9.89{col 59}{space 3}0.000{col 67}{space 4} 1.131023{col 80}{space 3} 1.690183
{txt}{space 2}dsaworheardkilloutgroup {c |}{col 27}{res}{space 2} 1.349533{col 39}{space 2} .2526467{col 50}{space 1}    5.34{col 59}{space 3}0.000{col 67}{space 4} .8543541{col 80}{space 3} 1.844711
{txt}{space 17}hadcovid {c |}{col 27}{res}{space 2} .2501257{col 39}{space 2} .3064292{col 50}{space 1}    0.82{col 59}{space 3}0.414{col 67}{space 4}-.3504645{col 80}{space 3} .8507159
{txt}{space 16}diedcovid {c |}{col 27}{res}{space 2}-1.012559{col 39}{space 2}  .161737{col 50}{space 1}   -6.26{col 59}{space 3}0.000{col 67}{space 4}-1.329558{col 80}{space 3}-.6955603
{txt}{space 19}female {c |}{col 27}{res}{space 2}  1.07241{col 39}{space 2} .4124692{col 50}{space 1}    2.60{col 59}{space 3}0.009{col 67}{space 4} .2639856{col 80}{space 3} 1.880835
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0085537{col 39}{space 2} .0077067{col 50}{space 1}   -1.11{col 59}{space 3}0.267{col 67}{space 4}-.0236585{col 80}{space 3} .0065511
{txt}{space 13}professional {c |}{col 27}{res}{space 2}-.2081477{col 39}{space 2} .1457658{col 50}{space 1}   -1.43{col 59}{space 3}0.153{col 67}{space 4}-.4938435{col 80}{space 3} .0775481
{txt}{space 18}laborer {c |}{col 27}{res}{space 2}-.1438658{col 39}{space 2} .3874485{col 50}{space 1}   -0.37{col 59}{space 3}0.710{col 67}{space 4}-.9032509{col 80}{space 3} .6155193
{txt}{space 15}unemployed {c |}{col 27}{res}{space 2}-.2341884{col 39}{space 2} .1432823{col 50}{space 1}   -1.63{col 59}{space 3}0.102{col 67}{space 4}-.5150165{col 80}{space 3} .0466398
{txt}{space 16}westmosul {c |}{col 27}{res}{space 2}-.4527717{col 39}{space 2}  .243219{col 50}{space 1}   -1.86{col 59}{space 3}0.063{col 67}{space 4}-.9294721{col 80}{space 3} .0239288
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} -2.41446{col 39}{space 2} .7091721{col 50}{space 1}   -3.40{col 59}{space 3}0.001{col 67}{space 4}-3.804412{col 80}{space 3}-1.024508
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Table 3.  Empathy, Victimization Priming, and Out-group Altruism (OLS Regression)
. 
. reg closeoutgroup victimorder , robust

{txt}Linear regression                               Number of obs     = {res}       278
                                                {txt}F(1, 276)         =  {res}    26.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1083
                                                {txt}Root MSE          =    {res} 1.4958

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}closeoutgr~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} 1.138784{col 26}{space 2} .2204513{col 37}{space 1}    5.17{col 46}{space 3}0.000{col 54}{space 4} .7048041{col 67}{space 3} 1.572763
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.418699{col 26}{space 2} .1981983{col 37}{space 1}   12.20{col 46}{space 3}0.000{col 54}{space 4} 2.028527{col 67}{space 3} 2.808872
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg closeoutgroup revdgsunnibias , robust

{txt}Linear regression                               Number of obs     = {res}       278
                                                {txt}F(1, 276)         =  {res}    31.73
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0864
                                                {txt}Root MSE          =    {res} 1.5141

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1} closeoutgroup{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} 1.090294{col 28}{space 2} .1935583{col 39}{space 1}    5.63{col 48}{space 3}0.000{col 56}{space 4} .7092564{col 69}{space 3} 1.471333
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.962736{col 28}{space 2} .1080759{col 39}{space 1}   27.41{col 48}{space 3}0.000{col 56}{space 4} 2.749978{col 69}{space 3} 3.175494
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg closeoutgroup victimorder revdgsunnibias dsaworheardkilloutgroup mmx_tetraisisvictimall mmx_tetrapreisisvictim hadcovid diedcovid female age professional laborer unemployed westmosul, cluster(location)

{txt}Linear regression                               Number of obs     = {res}       278
                                                {txt}{help j_robustsingular:F(4, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.2686
                                                {txt}Root MSE          =    {res} 1.3851

{txt}{ralign 91:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}            closeoutgroup{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}victimorder {c |}{col 27}{res}{space 2} .8564189{col 39}{space 2}   .17558{col 50}{space 1}    4.88{col 59}{space 3}0.005{col 67}{space 4} .4050761{col 80}{space 3} 1.307762
{txt}{space 11}revdgsunnibias {c |}{col 27}{res}{space 2} .9296706{col 39}{space 2} .1393369{col 50}{space 1}    6.67{col 59}{space 3}0.001{col 67}{space 4} .5714938{col 80}{space 3} 1.287848
{txt}{space 2}dsaworheardkilloutgroup {c |}{col 27}{res}{space 2}-.2389657{col 39}{space 2} .4253422{col 50}{space 1}   -0.56{col 59}{space 3}0.598{col 67}{space 4}-1.332343{col 80}{space 3} .8544113
{txt}{space 3}mmx_tetraisisvictimall {c |}{col 27}{res}{space 2}-.1257211{col 39}{space 2} .6179439{col 50}{space 1}   -0.20{col 59}{space 3}0.847{col 67}{space 4}-1.714197{col 80}{space 3} 1.462754
{txt}mmx_tetrapreisisvictimall {c |}{col 27}{res}{space 2} .6184523{col 39}{space 2} .0687862{col 50}{space 1}    8.99{col 59}{space 3}0.000{col 67}{space 4} .4416318{col 80}{space 3} .7952727
{txt}{space 17}hadcovid {c |}{col 27}{res}{space 2}-.1994803{col 39}{space 2} .2333306{col 50}{space 1}   -0.85{col 59}{space 3}0.432{col 67}{space 4}-.7992757{col 80}{space 3} .4003152
{txt}{space 16}diedcovid {c |}{col 27}{res}{space 2} .6993531{col 39}{space 2} .1129973{col 50}{space 1}    6.19{col 59}{space 3}0.002{col 67}{space 4} .4088842{col 80}{space 3}  .989822
{txt}{space 19}female {c |}{col 27}{res}{space 2} .2357776{col 39}{space 2} .3884444{col 50}{space 1}    0.61{col 59}{space 3}0.570{col 67}{space 4}-.7627504{col 80}{space 3} 1.234306
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0201402{col 39}{space 2} .0069324{col 50}{space 1}   -2.91{col 59}{space 3}0.034{col 67}{space 4}-.0379606{col 80}{space 3}-.0023199
{txt}{space 13}professional {c |}{col 27}{res}{space 2} .0627713{col 39}{space 2} .0644311{col 50}{space 1}    0.97{col 59}{space 3}0.375{col 67}{space 4}-.1028542{col 80}{space 3} .2283968
{txt}{space 18}laborer {c |}{col 27}{res}{space 2}-.1763774{col 39}{space 2} .0464572{col 50}{space 1}   -3.80{col 59}{space 3}0.013{col 67}{space 4}-.2957994{col 80}{space 3}-.0569554
{txt}{space 15}unemployed {c |}{col 27}{res}{space 2}-.3369912{col 39}{space 2} .1917773{col 50}{space 1}   -1.76{col 59}{space 3}0.139{col 67}{space 4}-.8299705{col 80}{space 3} .1559881
{txt}{space 16}westmosul {c |}{col 27}{res}{space 2} .0167537{col 39}{space 2} .0519787{col 50}{space 1}    0.32{col 59}{space 3}0.760{col 67}{space 4}-.1168618{col 80}{space 3} .1503691
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 2.813502{col 39}{space 2} .5571198{col 50}{space 1}    5.05{col 59}{space 3}0.004{col 67}{space 4}  1.38138{col 80}{space 3} 4.245624
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Online Appendix Replication
. 
. *Summary Statistics
. 
. sum perdgsunni perdgshia perdgyazidi perdgkurd perdgchristian perdgforeign closeshia closeyazidi closekurd closechristian closeforeigner closegay closeoutgroup sawkillsunni heardkilloutgroup sawkilloutgroup punishedisis dsexassault fampunishedisis faminjuredisis famkilledisis womenabusedisis fleehomeisis homedamagedisis injuredlib faminjuredlib famkilledlib homedamagedlib imprisonedlib fledhomelib homelootedlib iraniraq gulfwar saddampre90  iraqwar insurgency03 crime03 hadcovid diedcovid female age professional laborer unemployed westmosul  if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}perdgsunni {c |}{res}        278    74.67626    24.60305          0        100
{txt}{space 3}perdgshia {c |}{res}        278    3.884892    9.221368          0         50
{txt}{space 1}perdgyazidi {c |}{res}        278    15.39568    17.20617          0         80
{txt}{space 3}perdgkurd {c |}{res}        278    3.597122    9.614009          0         60
{txt}perdgchris~n {c |}{res}        278    .8633094    3.982274          0         30
{txt}{hline 13}{c +}{hline 57}
perdgforeign {c |}{res}        278           0           0          0          0
{txt}{space 3}closeshia {c |}{res}        278    4.471223    2.871037          0         10
{txt}{space 1}closeyazidi {c |}{res}        278    3.992806    2.974006          0         10
{txt}{space 3}closekurd {c |}{res}        275    5.650909    3.232187          0         10
{txt}closechris~n {c |}{res}        278    4.593525    3.063155          0         10
{txt}{hline 13}{c +}{hline 57}
closeforei~r {c |}{res}        276    .4492754    1.259567          0         10
{txt}{space 4}closegay {c |}{res}        276    .0942029    .3792372          0          3
{txt}closeoutgr~p {c |}{res}        278    3.221583    1.581169          0        7.5
{txt}sawkillsunni {c |}{res}        278    .2086331    .4070643          0          1
{txt}heardkillo~p {c |}{res}        278    .0791367    .2704388          0          1
{txt}{hline 13}{c +}{hline 57}
sawkillout~p {c |}{res}        278    .0215827    .1455787          0          1
{txt}punishedisis {c |}{res}        278    .1834532    .3877357          0          1
{txt}{space 1}dsexassault {c |}{res}        278     .057554    .2333181          0          1
{txt}fampunishe~s {c |}{res}        278    .0431655     .203596          0          1
{txt}faminjured~s {c |}{res}        278    .0683453    .2527926          0          1
{txt}{hline 13}{c +}{hline 57}
famkilledi~s {c |}{res}        278    .0179856    .1331386          0          1
{txt}womenabuse~s {c |}{res}        278    .3884892    .4882858          0          1
{txt}fleehomeisis {c |}{res}        278    .0971223    .2966583          0          1
{txt}homedamage~s {c |}{res}        278    .1978417    .3990906          0          1
{txt}{space 2}injuredlib {c |}{res}        278     .028777    .1674806          0          1
{txt}{hline 13}{c +}{hline 57}
faminjured~b {c |}{res}        278    .0647482    .2465248          0          1
{txt}famkilledlib {c |}{res}        278    .0143885    .1193007          0          1
{txt}homedamage~b {c |}{res}        278    .0503597    .2190805          0          1
{txt}imprisoned~b {c |}{res}        278    .1402878    .3479116          0          1
{txt}{space 1}fledhomelib {c |}{res}        278    .0791367    .2704388          0          1
{txt}{hline 13}{c +}{hline 57}
homelooted~b {c |}{res}        278    .2014388    .4017984          0          1
{txt}{space 4}iraniraq {c |}{res}        278    .1510791    .3587719          0          1
{txt}{space 5}gulfwar {c |}{res}        278    .0179856    .1331386          0          1
{txt}{space 1}saddampre90 {c |}{res}        278    .0359712    .1865542          0          1
{txt}{space 5}iraqwar {c |}{res}        278    .0323741    .1773108          0          1
{txt}{hline 13}{c +}{hline 57}
insurgency03 {c |}{res}        278     .205036     .404456          0          1
{txt}{space 5}crime03 {c |}{res}        278    .3129496    .4645303          0          1
{txt}{space 4}hadcovid {c |}{res}        278    .4856115    .5006943          0          1
{txt}{space 3}diedcovid {c |}{res}        278    .3129496    .4645303          0          1
{txt}{space 6}female {c |}{res}        278    .1510791    .3587719          0          1
{txt}{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        278    33.96043    13.88615         18         84
{txt}professional {c |}{res}        278     .528777    .5000714          0          1
{txt}{space 5}laborer {c |}{res}        278    .0935252    .2916921          0          1
{txt}{space 2}unemployed {c |}{res}        278    .1043165     .306222          0          1
{txt}{space 3}westmosul {c |}{res}        278    .3489209    .4774885          0          1
{txt}
{com}. 
. *ISIS Victimization Index 
. 
. *Tetrachoric Analysis of Victimization Experiences
. 
. tetrachoric punishedisis-womenabusedlib dsexassault, posdef
{res}{txt}(obs=278)

matrix with tetrachoric correlations is not positive semidefinite;
  it has {res}7{txt} negative eigenvalues
  maxdiff(corr,adj-corr) = {res} 1.0137
{txt}  (adj-corr: tetrachoric correlations adjusted to be positive semidefinite)

{ralign 12:adj-corr} {c |} punish~s fampun~s injure~s faminj~s famkil~s impris~s fleeho~s homeda~s
{hline 13}{c +}{hline 72}
punishedisis {c |} {res}  1.0000 
{txt}fampunishe~s {c |} {res} -0.0945   1.0000 
 {txt}injuredisis {c |} {res} -0.1838  -0.1086   1.0000 
{txt}faminjured~s {c |} {res} -0.1346  -0.0677  -0.0426   1.0000 
{txt}famkilledi~s {c |} {res}  0.3360  -0.1557  -0.1336  -0.1638   1.0000 
{txt}imprisoned~s {c |} {res} -0.1081   0.0137  -0.1129  -0.0764  -0.0665   1.0000 
{txt}fleehomeisis {c |} {res} -0.1645  -0.1300  -0.2144  -0.1182  -0.2279  -0.0984   1.0000 
{txt}homedamage~s {c |} {res} -0.3115  -0.2547  -0.1969  -0.1985  -0.2282  -0.2222   0.1516   1.0000 
{txt}womenabuse~s {c |} {res} -0.2242  -0.1692  -0.1404  -0.1537  -0.1691  -0.1268   0.0543  -0.0943 
  {txt}injuredlib {c |} {res}  0.3204  -0.2446   0.6242  -0.2255  -0.0700  -0.1617  -0.2137  -0.2995 
{txt}faminjured~b {c |} {res} -0.3590   0.3851   0.4168   0.6716  -0.1480  -0.1487  -0.1863  -0.3377 
{txt}famkilledlib {c |} {res} -0.1232  -0.1640  -0.2642  -0.1386   0.5358  -0.1604  -0.0903   0.4766 
{txt}homedamage~b {c |} {res}  0.0472  -0.3541  -0.4208   0.3184  -0.1402  -0.2442   0.3613   0.3664 
{txt}imprisoned~b {c |} {res}  0.3927   0.4485  -0.2304  -0.2352  -0.0779   0.6453  -0.2711  -0.2581 
 {txt}fledhomelib {c |} {res}  0.0590   0.2765  -0.3173   0.2278  -0.1402  -0.3428   0.4121   0.1816 
{txt}homelooted~b {c |} {res} -0.3725  -0.2220  -0.2103  -0.2262  -0.1416  -0.1735   0.3624   0.8714 
{txt}womenabuse~b {c |} {res} -0.0755  -0.1805  -0.1788  -0.1421  -0.1025  -0.1379   0.0369  -0.0597 
 {txt}dsexassault {c |} {res}  0.1213  -0.2595   0.4469  -0.3789  -0.2693  -0.1799  -0.2993   0.3173 

{txt}{ralign 12:adj-corr} {c |} womena~s injure~b faminj~b famkil~b homeda~b impris~b fledho~b homelo~b
{hline 13}{c +}{hline 72}
womenabuse~s {c |} {res}  1.0000 
  {txt}injuredlib {c |} {res}  0.1647   1.0000 
{txt}faminjured~b {c |} {res} -0.2786  -0.1236   1.0000 
{txt}famkilledlib {c |} {res} -0.2517  -0.2887  -0.2395   1.0000 
{txt}homedamage~b {c |} {res} -0.0153  -0.2684  -0.2594   0.3866   1.0000 
{txt}imprisoned~b {c |} {res} -0.2658  -0.1027  -0.2021  -0.3492  -0.3280   1.0000 
 {txt}fledhomelib {c |} {res} -0.3954  -0.3053   0.2231   0.2874   0.4024  -0.2350   1.0000 
{txt}homelooted~b {c |} {res} -0.0575  -0.2330  -0.3254   0.3623   0.1956  -0.2723   0.1195   1.0000 
{txt}womenabuse~b {c |} {res}  0.8273  -0.1158  -0.2292  -0.2662   0.0355  -0.1560  -0.3310  -0.1993 
 {txt}dsexassault {c |} {res}  0.2157   0.4808  -0.2394  -0.3465  -0.3550   0.0610  -0.5158   0.2219 

{txt}{ralign 12:adj-corr} {c |} womena~b dsexas~t
{hline 13}{c +}{hline 18}
womenabuse~b {c |} {res}  1.0000 
 {txt}dsexassault {c |} {res}  0.2611   1.0000 
{txt}
{com}. matrix C = r(Rho)
{txt}
{com}. factormat C, n(278) ipf factor(1)
{txt}(obs=278)
(collinear variables specified)

Factor analysis/correlation{col 50}Number of obs    = {res}       278
{col 5}{txt}Method: iterated principal factors{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      3.01129      0.80990            1.0000       1.0000
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      2.20139      0.71814            0.7310       1.7311
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.48326      0.26994            0.4926       2.2236
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      1.21331      0.15814            0.4029       2.6265
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      1.05517      0.43456            0.3504       2.9769
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.62061      0.31818            0.2061       3.1830
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.30242      0.12616            0.1004       3.2835
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}      0.17626      0.40431            0.0585       3.3420
{txt}{col 5}{ralign 11:Factor9}  {c |}{res}     -0.22805      0.17896           -0.0757       3.2663
{txt}{col 5}{ralign 11:Factor10}  {c |}{res}     -0.40700      0.08075           -0.1352       3.1311
{txt}{col 5}{ralign 11:Factor11}  {c |}{res}     -0.48775      0.18138           -0.1620       2.9691
{txt}{col 5}{ralign 11:Factor12}  {c |}{res}     -0.66913      0.08622           -0.2222       2.7469
{txt}{col 5}{ralign 11:Factor13}  {c |}{res}     -0.75536      0.08161           -0.2508       2.4961
{txt}{col 5}{ralign 11:Factor14}  {c |}{res}     -0.83696      0.03752           -0.2779       2.2181
{txt}{col 5}{ralign 11:Factor15}  {c |}{res}     -0.87448      0.03001           -0.2904       1.9277
{txt}{col 5}{ralign 11:Factor16}  {c |}{res}     -0.90449      0.01959           -0.3004       1.6274
{txt}{col 5}{ralign 11:Factor17}  {c |}{res}     -0.92408      0.04106           -0.3069       1.3205
{txt}{col 5}{ralign 11:Factor18}  {c |}{res}     -0.96513            .           -0.3205       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated: chi2({res}153{txt}) ={res} 6.7e+04{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:punishedisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.2178}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9526}}}{space 1}
{space 4}{space 0}{ralign 12:fampunishe~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1572}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9753}}}{space 1}
{space 4}{space 0}{ralign 12:injuredisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.4565}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7916}}}{space 1}
{space 4}{space 0}{ralign 12:faminjured~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0910}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9917}}}{space 1}
{space 4}{space 0}{ralign 12:famkilledi~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0188}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9996}}}{space 1}
{space 4}{space 0}{ralign 12:imprisoned~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.2476}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9387}}}{space 1}
{space 4}{space 0}{ralign 12:fleehomeisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4333}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8122}}}{space 1}
{space 4}{space 0}{ralign 12:homedamage~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6450}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5840}}}{space 1}
{space 4}{space 0}{ralign 12:womenabuse~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1346}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9819}}}{space 1}
{space 4}{space 0}{ralign 12:injuredlib}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.4907}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7593}}}{space 1}
{space 4}{space 0}{ralign 12:faminjured~b}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1621}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9737}}}{space 1}
{space 4}{space 0}{ralign 12:famkilledlib}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6129}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6243}}}{space 1}
{space 4}{space 0}{ralign 12:homedamage~b}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6683}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5534}}}{space 1}
{space 4}{space 0}{ralign 12:imprisoned~b}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.3945}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8444}}}{space 1}
{space 4}{space 0}{ralign 12:fledhomelib}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5454}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7025}}}{space 1}
{space 4}{space 0}{ralign 12:homelooted~b}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6044}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6347}}}{space 1}
{space 4}{space 0}{ralign 12:womenabuse~b}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1209}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9854}}}{space 1}
{space 4}{space 0}{ralign 12:dsexassault}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.3414}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8834}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. kdensity mmx_tetraisisvictim
{res}{txt}
{com}. 
. *Code to generate tetrachoric ISIS victim index in READ ME file. (already generated)
. *Code to generate factor analysis ISIS victim index in READ ME file. (already generated)
. *Code to generate additive ISIS victim index in READ ME file. (already generated)
. *Code to generate SEM ISIS victim index in READ ME file.(already generated)
. 
. *ISIS Victimization and Out-group Altruism (Logit Regression)
. 
. logit revdgsunnibias mmx_tetraisisvictimall , cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.01365}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-144.88816}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-144.88814}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-144.88814}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:94.04}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-144.88814}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0491}

{txt}{ralign 88:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        revdgsunnibias{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_tetraisisvictimall {c |}{col 24}{res}{space 2} 3.426901{col 36}{space 2} .3533858{col 47}{space 1}    9.70{col 56}{space 3}0.000{col 64}{space 4} 2.734278{col 77}{space 3} 4.119524
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-2.424147{col 36}{space 2} .1066529{col 47}{space 1}  -22.73{col 56}{space 3}0.000{col 64}{space 4}-2.633183{col 77}{space 3}-2.215112
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_factorisisvictimall , cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-150.35697}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-150.34257}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-150.34257}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:4.27}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0388}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-150.34257}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0133}

{txt}{ralign 89:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         revdgsunnibias{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      z{col 57}   P>|z|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_factorisisvictimall {c |}{col 25}{res}{space 2} .8687863{col 37}{space 2} .4205364{col 48}{space 1}    2.07{col 57}{space 3}0.039{col 65}{space 4} .0445502{col 78}{space 3} 1.693022
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}-1.606592{col 37}{space 2} .1353027{col 48}{space 1}  -11.87{col 57}{space 3}0.000{col 65}{space 4} -1.87178{col 78}{space 3}-1.341403
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_alphaisisvictimall , cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-147.92547}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-147.85439}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-147.85434}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-147.85434}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:4.60}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0320}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-147.85434}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0296}

{txt}{ralign 88:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        revdgsunnibias{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_alphaisisvictimall {c |}{col 24}{res}{space 2} 1.868747{col 36}{space 2} .8712317{col 47}{space 1}    2.14{col 56}{space 3}0.032{col 64}{space 4} .1611645{col 77}{space 3}  3.57633
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-2.008973{col 36}{space 2} .3498817{col 47}{space 1}   -5.74{col 56}{space 3}0.000{col 64}{space 4}-2.694728{col 77}{space 3}-1.323217
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias i.addisisvictim3, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-150.36189}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-150.27247}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-150.27247}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:166.31}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-150.27247}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0137}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
addisisvictim3 {c |}
{space 12}2  {c |}{col 16}{res}{space 2}-.1467766{col 28}{space 2} .2130112{col 39}{space 1}   -0.69{col 48}{space 3}0.491{col 56}{space 4} -.564271{col 69}{space 3} .2707177
{txt}{space 12}3  {c |}{col 16}{res}{space 2} .9162908{col 28}{space 2} .2867839{col 39}{space 1}    3.20{col 48}{space 3}0.001{col 56}{space 4} .3542047{col 69}{space 3} 1.478377
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-1.139434{col 28}{space 2} .1882684{col 39}{space 1}   -6.05{col 48}{space 3}0.000{col 56}{space 4}-1.508434{col 69}{space 3}-.7704349
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_semisisvictimall, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-140.12133}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-139.17169}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-139.16708}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-139.16708}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:54.97}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-139.16708}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0866}

{txt}{ralign 86:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}      revdgsunnibias{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      z{col 54}   P>|z|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_semisisvictimall {c |}{col 22}{res}{space 2} 3.855875{col 34}{space 2} .5200582{col 45}{space 1}    7.41{col 54}{space 3}0.000{col 62}{space 4}  2.83658{col 75}{space 3}  4.87517
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-3.798965{col 34}{space 2}  .613923{col 45}{space 1}   -6.19{col 54}{space 3}0.000{col 62}{space 4}-5.002231{col 75}{space 3}-2.595698
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Pre-ISIS Victimization Index 
. 
. *Code to generate tetrachoric Pre-ISIS victim index for Mosul in READ ME file. (already generated)
. *Code to generate factor analysis Pre-ISIS victim index for Mosul in READ ME file. (already generated)
. *Code to generate alpha Pre-ISIS victim index for Mosul in READ ME file. (already generated)
. *Code to generate additive Pre-ISIS victim index for Mosul in READ ME file.(already generated)
. *Code to generate SEM Pre-ISIS victim index for Mosul in READ ME file. (already generated)
. 
. *Tetrachoric Correlation Matrix of Pre-ISIS-related Victimization Experience
. 
. tetrachoric iraniraq gulfwar saddampre90 iraqwar insurgency03 crime03 if mosul==1, posdef
{res}{txt}(obs=278)

matrix with tetrachoric correlations is not positive semidefinite;
  it has {res}1{txt} negative eigenvalue
  maxdiff(corr,adj-corr) = {res} 0.8093
{txt}  (adj-corr: tetrachoric correlations adjusted to be positive semidefinite)

{ralign 12:adj-corr} {c |} iraniraq  gulfwar sadd~e90  iraqwar insur~03  crime03
{hline 13}{c +}{hline 54}
    iraniraq {c |} {res}  1.0000 
     {txt}gulfwar {c |} {res} -0.1907   1.0000 
 {txt}saddampre90 {c |} {res} -0.1907  -0.1907   1.0000 
     {txt}iraqwar {c |} {res} -0.2683  -0.2683  -0.2683   1.0000 
{txt}insurgency03 {c |} {res} -0.2683  -0.2683  -0.2683   0.3457   1.0000 
     {txt}crime03 {c |} {res} -0.1907  -0.1907  -0.1907  -0.2683  -0.2683   1.0000 
{txt}
{com}. matrix C = r(Rho)
{txt}
{com}. factormat C, n(278) ipf factor(1)
{txt}(obs=278)
(collinear variables specified)

Factor analysis/correlation{col 50}Number of obs    = {res}       278
{col 5}{txt}Method: iterated principal factors{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.17436      0.93477            0.9999       0.9999
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.23960      0.00000            0.2040       1.2039
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.23960      0.00000            0.2040       1.4079
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.23960      0.09587            0.2040       1.6119
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.14372      1.00614            0.1224       1.7343
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.86242            .           -0.7343       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}15{txt}) ={res}       .{txt} Prob>chi2 ={res}      .

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:iraniraq}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.2211}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9511}}}{space 1}
{space 4}{space 0}{ralign 12:gulfwar}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.2211}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9511}}}{space 1}
{space 4}{space 0}{ralign 12:saddampre90}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.2211}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9511}}}{space 1}
{space 4}{space 0}{ralign 12:iraqwar}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6996}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5106}}}{space 1}
{space 4}{space 0}{ralign 12:insurgency03}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6996}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5106}}}{space 1}
{space 4}{space 0}{ralign 12:crime03}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.2211}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9511}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. kdensity mmx_tetrapreisisvictimall
{res}{txt}
{com}. 
. *Pre-ISIS Victimization and Out-group Altruism (Logit Regression)
. 
. logit revdgsunnibias mmx_tetrapreisisvictimall, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-147.92127}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-147.84262}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-147.84261}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:67.41}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-147.84261}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0297}

{txt}{ralign 91:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}           revdgsunnibias{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_tetrapreisisvictimall {c |}{col 27}{res}{space 2} 1.056339{col 39}{space 2} .1286577{col 50}{space 1}    8.21{col 59}{space 3}0.000{col 67}{space 4} .8041747{col 80}{space 3} 1.308504
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}-1.527548{col 39}{space 2} .2229631{col 50}{space 1}   -6.85{col 59}{space 3}0.000{col 67}{space 4}-1.964547{col 80}{space 3}-1.090548
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_factorpreisisvictimall, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.92632}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.77017}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.76987}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.76987}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:46.00}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.76987}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0433}

{txt}{ralign 92:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}            revdgsunnibias{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_factorpreisisvictimall {c |}{col 28}{res}{space 2} 1.660724{col 40}{space 2} .2448518{col 51}{space 1}    6.78{col 60}{space 3}0.000{col 68}{space 4} 1.180824{col 81}{space 3} 2.140625
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-2.003312{col 40}{space 2}  .168217{col 51}{space 1}  -11.91{col 60}{space 3}0.000{col 68}{space 4}-2.333012{col 81}{space 3}-1.673613
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_alphapreisisvictimall, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.58564}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.43651}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.43631}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.43631}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:88.79}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.43631}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0455}

{txt}{ralign 91:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}           revdgsunnibias{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_alphapreisisvictimall {c |}{col 27}{res}{space 2} 1.915461{col 39}{space 2} .2032838{col 50}{space 1}    9.42{col 59}{space 3}0.000{col 67}{space 4} 1.517033{col 80}{space 3}  2.31389
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}-1.968206{col 39}{space 2} .1613289{col 50}{space 1}  -12.20{col 59}{space 3}0.000{col 67}{space 4}-2.284405{col 80}{space 3}-1.652007
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias addpreisisvictimall if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-152.32884}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-152.32883}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:0.63}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.4276}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-152.32883}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0002}

{txt}{ralign 85:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}     revdgsunnibias{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
addpreisisvictimall {c |}{col 21}{res}{space 2}-.0846365{col 33}{space 2} .1066967{col 44}{space 1}   -0.79{col 53}{space 3}0.428{col 61}{space 4}-.2937581{col 74}{space 3} .1244852
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.103371{col 33}{space 2} .2643111{col 44}{space 1}   -4.17{col 53}{space 3}0.000{col 61}{space 4}-1.621411{col 74}{space 3}-.5853311
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_sempreisisvictimall if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-148.31666}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-148.26662}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -148.2666}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -148.2666}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:15.92}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-148.2666}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0269}

{txt}{ralign 89:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         revdgsunnibias{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      z{col 57}   P>|z|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_sempreisisvictimall {c |}{col 25}{res}{space 2} 1.778339{col 37}{space 2} .4457053{col 48}{space 1}    3.99{col 57}{space 3}0.000{col 65}{space 4} .9047727{col 78}{space 3} 2.651906
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}-1.966783{col 37}{space 2} .1067306{col 48}{space 1}  -18.43{col 57}{space 3}0.000{col 65}{space 4}-2.175971{col 78}{space 3}-1.757595
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Code to generate awareness of others' suffering index in Mosul in READ ME file. (already generated)
. 
. *Balance tests
. 
. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(dheardkilloutgroup) savexlsx(balancedheardkilloutroup) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancedheardkilloutroup.xlsx":balancedheardkilloutroup.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(dsawkilloutgroup) savexlsx(balancedsawkilloutroup) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancedsawkilloutroup.xlsx":balancedsawkilloutroup.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(sawkillsunni) savexlsx(balancesawkillsunni) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancesawkillsunni.xlsx":balancesawkillsunni.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(punishedisis) savexlsx(balancepunishedisis) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancepunishedisis.xlsx":balancepunishedisis.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(injuredlib) savexlsx(balanceinjuredlib) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balanceinjuredlib.xlsx":balanceinjuredlib.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(imprisonedlib) savexlsx(balanceimprisonedlib) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balanceimprisonedlib.xlsx":balanceimprisonedlib.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(dsexassault) savexlsx(balancedsexassault) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancedsexassault.xlsx":balancedsexassault.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(fampunishedisis) savexlsx(balancefampunishedisis) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancefampunishedisis.xlsx":balancefampunishedisis.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(faminjuredisis) savexlsx(balancefaminjuredisis) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancefaminjuredisis.xlsx":balancefaminjuredisis.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(faminjuredlib) savexlsx(balancefaminjuredlib) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancefaminjuredlib.xlsx":balancefaminjuredlib.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(womenabusedisis) savexlsx(balancewomenabusedisis) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancewomenabusedisis.xlsx":balancewomenabusedisis.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(fleehomeisis) savexlsx(balancefleehomeisis) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancefleehomeisis.xlsx":balancefleehomeisis.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(homedamagedisis) savexlsx(balancehomedamagedisis) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancehomedamagedisis.xlsx":balancehomedamagedisis.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(fledhomelib) savexlsx(balancefledhomelib) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancefledhomelib.xlsx":balancefledhomelib.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(homedamagedlib) savexlsx(balancehomedamagedlib) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancehomedamagedlib.xlsx":balancehomedamagedlib.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(homelootedlib) savexlsx(balancehomelootedlib) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancehomelootedlib.xlsx":balancehomelootedlib.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(iraniraq) savexlsx(balanceiraniraq) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balanceiraniraq.xlsx":balanceiraniraq.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(gulfwar) savexlsx(balancegulfwar) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancegulfwar.xlsx":balancegulfwar.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(saddampre90) savexlsx(balancesaddampre90) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancesaddampre90.xlsx":balancesaddampre90.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(iraqwar) savexlsx(balanceiraqwar) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balanceiraqwar.xlsx":balanceiraqwar.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(insurgency03) savexlsx(balanceinsurgency03) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balanceinsurgency03.xlsx":balanceinsurgency03.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(crime03) savexlsx(balancecrime03) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancecrime03.xlsx":balancecrime03.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(hadcovid) savexlsx(balancehadcovid) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancehadcovid.xlsx":balancehadcovid.xlsx}{p_end}
{txt}
{com}. iebaltab female age professional laborer unemployed westmosul  if mosul==1, groupvar(diedcovid) savexlsx(balancediedcovid) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancediedcovid.xlsx":balancediedcovid.xlsx}{p_end}
{txt}
{com}. 
. *Correlational Analysis of Outgroup Altruism 
. 
. pwcorr revdgsunnibias dheardkilloutgroup dsawkilloutgroup sawkillsunni punishedisis injuredisis imprisonedisis dsexassault fampunishedisis faminjuredisis fleehomeisis homedamagedisis fledhomelib homedamagedlib homelootedlib iraniraq gulfwar saddampre90 iraqwar insurgency03 crime03 hadcovid diedcovid female age professional laborer unemployed westmosul  if mosul==1, sig

             {txt}{c |} revdgsu~ dheard~p dsawki~p sawki~ni punish~s injure~s impris~s
{hline 13}{c +}{hline 63}
revdgsunni~s {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
dheardkill~p {c |} {res}  0.2749   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
dsawkillou~p {c |} {res}  0.1498   0.3232   1.0000 
             {txt}{c |}{res}   0.0124   0.0000
             {txt}{c |}
sawkillsunni {c |} {res} -0.1200  -0.0849   0.0456   1.0000 
             {txt}{c |}{res}   0.0455   0.1579   0.4491
             {txt}{c |}
punishedisis {c |} {res} -0.1553  -0.0701  -0.0704  -0.2434   1.0000 
             {txt}{c |}{res}   0.0095   0.2441   0.2420   0.0000
             {txt}{c |}
 injuredisis {c |} {res}  0.0746   0.1884   0.3978  -0.0620  -0.0573   1.0000 
             {txt}{c |}{res}   0.2152   0.0016   0.0000   0.3027   0.3414
             {txt}{c |}
imprisoned~s {c |} {res} -0.0674   0.1884  -0.0179  -0.0620  -0.0573  -0.0146   1.0000 
             {txt}{c |}{res}   0.2626   0.0016   0.7658   0.3027   0.3414   0.8085
             {txt}{c |}
 dsexassault {c |} {res} -0.0653   0.0420   0.1759   0.3292  -0.0373   0.2295  -0.0299 
             {txt}{c |}{res}   0.2780   0.4857   0.0033   0.0000   0.5355   0.0001   0.6201
             {txt}{c |}
fampunishe~s {c |} {res}  0.1311   0.0689  -0.0315  -0.1091  -0.1007  -0.0257  -0.0257 
             {txt}{c |}{res}   0.0289   0.2524   0.6005   0.0694   0.0939   0.6701   0.6701
             {txt}{c |}
faminjured~s {c |} {res}  0.1169   0.0262  -0.0402  -0.1391  -0.1284  -0.0327  -0.0327 
             {txt}{c |}{res}   0.0515   0.6634   0.5042   0.0204   0.0324   0.5869   0.5869
             {txt}{c |}
fleehomeisis {c |} {res}  0.1881   0.3538   0.1185  -0.0488  -0.1555  -0.0396  -0.0396 
             {txt}{c |}{res}   0.0016   0.0000   0.0485   0.4175   0.0094   0.5105   0.5105
             {txt}{c |}
homedamage~s {c |} {res}  0.0837  -0.0118   0.0505   0.2783  -0.2354  -0.0600  -0.0600 
             {txt}{c |}{res}   0.1642   0.8448   0.4015   0.0000   0.0001   0.3188   0.3188
             {txt}{c |}
 fledhomelib {c |} {res}  0.3062   0.3089  -0.0435  -0.0849   0.0676  -0.0354  -0.0354 
             {txt}{c |}{res}   0.0000   0.0000   0.4697   0.1579   0.2612   0.5565   0.5565
             {txt}{c |}
homedamage~b {c |} {res}  0.1035   0.0544   0.1922   0.0437   0.0183  -0.0278  -0.0278 
             {txt}{c |}{res}   0.0851   0.3666   0.0013   0.4682   0.7607   0.6441   0.6441
             {txt}{c |}
homelooted~b {c |} {res}  0.0359  -0.0808  -0.0746   0.2277  -0.2381  -0.0607  -0.0607 
             {txt}{c |}{res}   0.5507   0.1792   0.2150   0.0001   0.0001   0.3134   0.3134
             {txt}{c |}
    iraniraq {c |} {res} -0.1410  -0.0493  -0.0627  -0.1672  -0.0442   0.1177  -0.0510 
             {txt}{c |}{res}   0.0187   0.4133   0.2979   0.0052   0.4625   0.0499   0.3972
             {txt}{c |}
     gulfwar {c |} {res}  0.1153  -0.0397  -0.0201  -0.0695   0.0757  -0.0164  -0.0164 
             {txt}{c |}{res}   0.0548   0.5100   0.7386   0.2482   0.2082   0.7861   0.7861
             {txt}{c |}
 saddampre90 {c |} {res}  0.2554   0.0865  -0.0287  -0.0041  -0.0916  -0.0233  -0.0233 
             {txt}{c |}{res}   0.0000   0.1504   0.6339   0.9457   0.1278   0.6984   0.6984
             {txt}{c |}
     iraqwar {c |} {res}  0.0890   0.2475   0.2525   0.0061   0.1759  -0.0221  -0.0221 
             {txt}{c |}{res}   0.1388   0.0000   0.0000   0.9191   0.0033   0.7137   0.7137
             {txt}{c |}
insurgency03 {c |} {res}  0.1564  -0.0169   0.0472  -0.1292   0.2427  -0.0614  -0.0614 
             {txt}{c |}{res}   0.0090   0.7796   0.4331   0.0313   0.0000   0.3080   0.3080
             {txt}{c |}
     crime03 {c |} {res} -0.2125  -0.0254  -0.1002   0.1880  -0.1596  -0.0815   0.0487 
             {txt}{c |}{res}   0.0004   0.6729   0.0953   0.0016   0.0077   0.1752   0.4182
             {txt}{c |}
    hadcovid {c |} {res} -0.0347  -0.0715   0.1529  -0.3926   0.3019   0.1244   0.1244 
             {txt}{c |}{res}   0.5647   0.2344   0.0107   0.0000   0.0000   0.0383   0.0383
             {txt}{c |}
   diedcovid {c |} {res} -0.0849   0.0320   0.0065  -0.2702   0.3616  -0.0815   0.0487 
             {txt}{c |}{res}   0.1582   0.5947   0.9137   0.0000   0.0000   0.1752   0.4182
             {txt}{c |}
      female {c |} {res}  0.1659   0.2484   0.2138  -0.0930   0.0077   0.1177  -0.0510 
             {txt}{c |}{res}   0.0055   0.0000   0.0003   0.1218   0.8989   0.0499   0.3972
             {txt}{c |}
         age {c |} {res} -0.0893   0.0316   0.0219  -0.0867  -0.0764  -0.0498   0.0483 
             {txt}{c |}{res}   0.1376   0.5998   0.7168   0.1495   0.2039   0.4084   0.4226
             {txt}{c |}
professional {c |} {res} -0.0660  -0.0436  -0.0582  -0.1537   0.1123  -0.0070  -0.0070 
             {txt}{c |}{res}   0.2725   0.4691   0.3340   0.0103   0.0615   0.9080   0.9080
             {txt}{c |}
     laborer {c |} {res} -0.0050  -0.0942  -0.0477  -0.0433   0.0393  -0.0388   0.1687 
             {txt}{c |}{res}   0.9337   0.1173   0.4282   0.4720   0.5144   0.5193   0.0048
             {txt}{c |}
  unemployed {c |} {res}  0.0585  -0.0129   0.1113   0.0565   0.0511  -0.0412  -0.0412 
             {txt}{c |}{res}   0.3312   0.8310   0.0639   0.3483   0.3963   0.4935   0.4935
             {txt}{c |}
   westmosul {c |} {res} -0.1069  -0.1307   0.0471   0.1628  -0.0155  -0.0251  -0.0885 
             {txt}{c |}{res}   0.0751   0.0293   0.4343   0.0065   0.7969   0.6772   0.1413
             {txt}{c |}

             {c |} dsexas~t fampun~s faminj~s fleeho~s homeda~s fledho~b homeda~b
{hline 13}{c +}{hline 63}
 dsexassault {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
fampunishe~s {c |} {res} -0.0525   1.0000 
             {txt}{c |}{res}   0.3833
             {txt}{c |}
faminjured~s {c |} {res} -0.0669  -0.0575   1.0000 
             {txt}{c |}{res}   0.2660   0.3393
             {txt}{c |}
fleehomeisis {c |} {res} -0.0811  -0.0697  -0.0888   1.0000 
             {txt}{c |}{res}   0.1778   0.2470   0.1396
             {txt}{c |}
homedamage~s {c |} {res}  0.1099  -0.1055  -0.1345  -0.1019   1.0000 
             {txt}{c |}{res}   0.0673   0.0791   0.0249   0.0899
             {txt}{c |}
 fledhomelib {c |} {res} -0.0724   0.0689   0.0262   0.2638  -0.0118   1.0000 
             {txt}{c |}{res}   0.2286   0.2524   0.6634   0.0000   0.8448
             {txt}{c |}
homedamage~b {c |} {res} -0.0569  -0.0489   0.1332   0.1467   0.1334   0.0544   1.0000 
             {txt}{c |}{res}   0.3445   0.4166   0.0264   0.0144   0.0262   0.3666
             {txt}{c |}
homelooted~b {c |} {res}  0.0299  -0.1067  -0.1360   0.0776   0.6961  -0.0808  -0.0336 
             {txt}{c |}{res}   0.6194   0.0758   0.0233   0.1972   0.0000   0.1792   0.5765
             {txt}{c |}
    iraniraq {c |} {res} -0.0180   0.1081  -0.0347  -0.0027  -0.0834  -0.1237  -0.0971 
             {txt}{c |}{res}   0.7652   0.0720   0.5651   0.9645   0.1653   0.0393   0.1060
             {txt}{c |}
     gulfwar {c |} {res} -0.0334  -0.0287  -0.0367   0.2298  -0.0672  -0.0397  -0.0312 
             {txt}{c |}{res}   0.5787   0.6332   0.5428   0.0001   0.2641   0.5100   0.6049
             {txt}{c |}
 saddampre90 {c |} {res} -0.0477  -0.0410  -0.0523  -0.0634   0.1950  -0.0566  -0.0445 
             {txt}{c |}{res}   0.4279   0.4957   0.3848   0.2925   0.0011   0.3469   0.4601
             {txt}{c |}
     iraqwar {c |} {res} -0.0452  -0.0389   0.1115   0.0773   0.0112  -0.0536   0.1437 
             {txt}{c |}{res}   0.4529   0.5189   0.0633   0.1990   0.8526   0.3731   0.0165
             {txt}{c |}
insurgency03 {c |} {res} -0.1255  -0.0202   0.0743  -0.0462  -0.1404   0.0161   0.0868 
             {txt}{c |}{res}   0.0365   0.7376   0.2168   0.4428   0.0192   0.7887   0.1491
             {txt}{c |}
     crime03 {c |} {res}  0.0997  -0.1433  -0.0598  -0.0118  -0.0626   0.0033  -0.1554 
             {txt}{c |}{res}   0.0972   0.0168   0.3203   0.8450   0.2987   0.9562   0.0094
             {txt}{c |}
    hadcovid {c |} {res} -0.1165  -0.0647  -0.0635   0.1431  -0.1212   0.0351   0.1712 
             {txt}{c |}{res}   0.0523   0.2823   0.2913   0.0169   0.0435   0.5600   0.0042
             {txt}{c |}
   diedcovid {c |} {res} -0.1002  -0.0670  -0.0598   0.0930  -0.0431   0.0608   0.0929 
             {txt}{c |}{res}   0.0956   0.2655   0.3203   0.1218   0.4744   0.3126   0.1223
             {txt}{c |}
      female {c |} {res} -0.0180  -0.0896  -0.1143  -0.0027   0.0931   0.0252   0.1784 
             {txt}{c |}{res}   0.7652   0.1362   0.0571   0.9645   0.1217   0.6762   0.0028
             {txt}{c |}
         age {c |} {res} -0.0762  -0.0198  -0.1196  -0.1226  -0.0598  -0.1674  -0.1370 
             {txt}{c |}{res}   0.2054   0.7421   0.0464   0.0410   0.3204   0.0051   0.0223
             {txt}{c |}
professional {c |} {res} -0.0761  -0.0832   0.0272  -0.1528   0.0528  -0.0703  -0.1451 
             {txt}{c |}{res}   0.2057   0.1667   0.6513   0.0108   0.3808   0.2428   0.0155
             {txt}{c |}
     laborer {c |} {res}  0.0267   0.4181   0.0109  -0.0219  -0.1595  -0.0026   0.0955 
             {txt}{c |}{res}   0.6574   0.0000   0.8562   0.7161   0.0077   0.9651   0.1121
             {txt}{c |}
  unemployed {c |} {res}  0.0167  -0.0725   0.0008   0.0470   0.1555   0.0307   0.2443 
             {txt}{c |}{res}   0.7813   0.2283   0.9889   0.4348   0.0094   0.6099   0.0000
             {txt}{c |}
   westmosul {c |} {res}  0.0783  -0.1555  -0.1385  -0.1636   0.0343  -0.1028   0.0385 
             {txt}{c |}{res}   0.1929   0.0094   0.0209   0.0062   0.5693   0.0872   0.5228
             {txt}{c |}

             {c |} homelo~b iraniraq  gulfwar sadd~e90  iraqwar insur~03  crime03
{hline 13}{c +}{hline 63}
homelooted~b {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
    iraniraq {c |} {res}  0.0386   1.0000 
             {txt}{c |}{res}   0.5220
             {txt}{c |}
     gulfwar {c |} {res} -0.0680  -0.0571   1.0000 
             {txt}{c |}{res}   0.2587   0.3429
             {txt}{c |}
 saddampre90 {c |} {res}  0.1920  -0.0815  -0.0261   1.0000 
             {txt}{c |}{res}   0.0013   0.1755   0.6643
             {txt}{c |}
     iraqwar {c |} {res} -0.0919  -0.0772  -0.0248  -0.0353   1.0000 
             {txt}{c |}{res}   0.1265   0.1996   0.6811   0.5574
             {txt}{c |}
insurgency03 {c |} {res} -0.0774  -0.2142  -0.0687  -0.0981   0.0078   1.0000 
             {txt}{c |}{res}   0.1985   0.0003   0.2534   0.1026   0.8972
             {txt}{c |}
     crime03 {c |} {res} -0.0682  -0.2847  -0.0913  -0.1304  -0.1234  -0.3428   1.0000 
             {txt}{c |}{res}   0.2572   0.0000   0.1287   0.0298   0.0397   0.0000
             {txt}{c |}
    hadcovid {c |} {res} -0.0394   0.1327   0.1393  -0.1104   0.0256  -0.0121  -0.0970 
             {txt}{c |}{res}   0.5132   0.0269   0.0202   0.0661   0.6709   0.8406   0.1066
             {txt}{c |}
   diedcovid {c |} {res}  0.0479   0.1269   0.0254  -0.1304   0.0957   0.0800  -0.0874 
             {txt}{c |}{res}   0.4266   0.0345   0.6732   0.0298   0.1114   0.1837   0.1459
             {txt}{c |}
      female {c |} {res} -0.0616  -0.0097  -0.0571   0.0264   0.1498  -0.1147  -0.0031 
             {txt}{c |}{res}   0.3060   0.8723   0.3429   0.6613   0.0124   0.0561   0.9587
             {txt}{c |}
         age {c |} {res}  0.0480   0.3121  -0.0426   0.1120   0.0240  -0.0898  -0.0753 
             {txt}{c |}{res}   0.4252   0.0000   0.4796   0.0621   0.6905   0.1352   0.2107
             {txt}{c |}
professional {c |} {res}  0.0788  -0.1048  -0.0349   0.1824   0.0912   0.0153  -0.0156 
             {txt}{c |}{res}   0.1899   0.0811   0.5621   0.0023   0.1291   0.7989   0.7957
             {txt}{c |}
     laborer {c |} {res} -0.0997   0.0025  -0.0435  -0.0620  -0.0588   0.1735  -0.1369 
             {txt}{c |}{res}   0.0971   0.9671   0.4704   0.3026   0.3290   0.0037   0.0225
             {txt}{c |}
  unemployed {c |} {res}  0.0340   0.0203  -0.0462  -0.0659   0.0706   0.0307   0.0488 
             {txt}{c |}{res}   0.5726   0.7358   0.4431   0.2733   0.2410   0.6100   0.4173
             {txt}{c |}
   westmosul {c |} {res}  0.0087  -0.0559  -0.0423  -0.0198   0.0367  -0.0353   0.1244 
             {txt}{c |}{res}   0.8857   0.3527   0.4826   0.7421   0.5427   0.5578   0.0382
             {txt}{c |}

             {c |} hadcovid diedco~d   female      age profes~l  laborer unempl~d
{hline 13}{c +}{hline 63}
    hadcovid {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
   diedcovid {c |} {res}  0.5394   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
      female {c |} {res}  0.0925   0.1485   1.0000 
             {txt}{c |}{res}   0.1238   0.0132
             {txt}{c |}
         age {c |} {res}  0.0640   0.0769   0.0142   1.0000 
             {txt}{c |}{res}   0.2873   0.2010   0.8130
             {txt}{c |}
professional {c |} {res}  0.1675   0.1243   0.0360   0.3482   1.0000 
             {txt}{c |}{res}   0.0051   0.0384   0.5495   0.0000
             {txt}{c |}
     laborer {c |} {res}  0.0340   0.1029  -0.1355  -0.1221  -0.3403   1.0000 
             {txt}{c |}{res}   0.5728   0.0867   0.0238   0.0420   0.0000
             {txt}{c |}
  unemployed {c |} {res}  0.0451  -0.0527   0.0861  -0.2376  -0.3615  -0.1096   1.0000 
             {txt}{c |}{res}   0.4534   0.3816   0.1524   0.0001   0.0000   0.0680
             {txt}{c |}
   westmosul {c |} {res} -0.0469  -0.1035  -0.0349   0.0467  -0.0346  -0.0796  -0.0029 
             {txt}{c |}{res}   0.4363   0.0851   0.5626   0.4377   0.5652   0.1856   0.9612
             {txt}{c |}

             {c |} westmo~l
{hline 13}{c +}{hline 9}
   westmosul {c |} {res}  1.0000 
             {txt}{c |}
             {c |}

{com}. 
. *Victimization and Altruism (One-at-a-Time Models)
. 
. *Awareness of Out-group Violence (Logit Regression)
. 
. logit revdgsunnibias dheardkilloutgroup female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-140.32534}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-139.72233}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-139.71791}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-139.71791}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-139.71791}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0830}

{txt}{ralign 84:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    revdgsunnibias{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dheardkilloutgroup {c |}{col 20}{res}{space 2} 1.727464{col 32}{space 2} .1201362{col 43}{space 1}   14.38{col 52}{space 3}0.000{col 60}{space 4} 1.492002{col 73}{space 3} 1.962927
{txt}{space 12}female {c |}{col 20}{res}{space 2} .6710928{col 32}{space 2} .3395272{col 43}{space 1}    1.98{col 52}{space 3}0.048{col 60}{space 4} .0056317{col 73}{space 3} 1.336554
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0173282{col 32}{space 2} .0069489{col 43}{space 1}   -2.49{col 52}{space 3}0.013{col 60}{space 4}-.0309478{col 73}{space 3}-.0037086
{txt}{space 6}professional {c |}{col 20}{res}{space 2}-.0724864{col 32}{space 2} .1045935{col 43}{space 1}   -0.69{col 52}{space 3}0.488{col 60}{space 4}-.2774859{col 73}{space 3} .1325131
{txt}{space 11}laborer {c |}{col 20}{res}{space 2} .1413566{col 32}{space 2} .3800282{col 43}{space 1}    0.37{col 52}{space 3}0.710{col 60}{space 4} -.603485{col 73}{space 3} .8861981
{txt}{space 8}unemployed {c |}{col 20}{res}{space 2} .2052929{col 32}{space 2} .1753881{col 43}{space 1}    1.17{col 52}{space 3}0.242{col 60}{space 4}-.1384614{col 73}{space 3} .5490473
{txt}{space 9}westmosul {c |}{col 20}{res}{space 2}-.3972368{col 32}{space 2} .1811236{col 43}{space 1}   -2.19{col 52}{space 3}0.028{col 60}{space 4}-.7522325{col 73}{space 3}-.0422412
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-.7664555{col 32}{space 2} .4110969{col 43}{space 1}   -1.86{col 52}{space 3}0.062{col 60}{space 4}-1.572191{col 73}{space 3} .0392795
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias dsawkilloutgroup female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-144.41935}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-144.17441}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-144.17324}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-144.17324}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-144.17324}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0538}

{txt}{ralign 82:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  revdgsunnibias{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dsawkilloutgroup {c |}{col 18}{res}{space 2}  1.68553{col 30}{space 2} .2565939{col 41}{space 1}    6.57{col 50}{space 3}0.000{col 58}{space 4} 1.182616{col 71}{space 3} 2.188445
{txt}{space 10}female {c |}{col 18}{res}{space 2} .8304627{col 30}{space 2} .3767404{col 41}{space 1}    2.20{col 50}{space 3}0.028{col 58}{space 4}  .092065{col 71}{space 3}  1.56886
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0149193{col 30}{space 2} .0058038{col 41}{space 1}   -2.57{col 50}{space 3}0.010{col 58}{space 4}-.0262946{col 71}{space 3} -.003544
{txt}{space 4}professional {c |}{col 18}{res}{space 2}-.2199773{col 30}{space 2} .2353674{col 41}{space 1}   -0.93{col 50}{space 3}0.350{col 58}{space 4} -.681289{col 71}{space 3} .2413344
{txt}{space 9}laborer {c |}{col 18}{res}{space 2}-.1046317{col 30}{space 2} .2535698{col 41}{space 1}   -0.41{col 50}{space 3}0.680{col 58}{space 4}-.6016193{col 71}{space 3} .3923559
{txt}{space 6}unemployed {c |}{col 18}{res}{space 2}-.0509404{col 30}{space 2} .0968798{col 41}{space 1}   -0.53{col 50}{space 3}0.599{col 58}{space 4}-.2408214{col 71}{space 3} .1389406
{txt}{space 7}westmosul {c |}{col 18}{res}{space 2}-.5970754{col 30}{space 2} .2694634{col 41}{space 1}   -2.22{col 50}{space 3}0.027{col 58}{space 4}-1.125214{col 71}{space 3}-.0689368
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.5520116{col 30}{space 2} .4669243{col 41}{space 1}   -1.18{col 50}{space 3}0.237{col 58}{space 4}-1.467167{col 71}{space 3} .3631432
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias sawkillsunni female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-144.22812}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-143.94653}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-143.94584}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-143.94584}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-143.94584}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0553}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}sawkillsunni {c |}{col 16}{res}{space 2}-.7956168{col 28}{space 2} .2033459{col 39}{space 1}   -3.91{col 48}{space 3}0.000{col 56}{space 4}-1.194167{col 69}{space 3}-.3970662
{txt}{space 8}female {c |}{col 16}{res}{space 2} .9042677{col 28}{space 2} .4270855{col 39}{space 1}    2.12{col 48}{space 3}0.034{col 56}{space 4} .0671954{col 69}{space 3}  1.74134
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0141435{col 28}{space 2}  .006839{col 39}{space 1}   -2.07{col 48}{space 3}0.039{col 56}{space 4}-.0275477{col 69}{space 3}-.0007393
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.3824169{col 28}{space 2}  .209288{col 39}{space 1}   -1.83{col 48}{space 3}0.068{col 56}{space 4}-.7926139{col 69}{space 3} .0277802
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.2537945{col 28}{space 2} .1892927{col 39}{space 1}   -1.34{col 48}{space 3}0.180{col 56}{space 4}-.6248014{col 69}{space 3} .1172125
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2}-.0016527{col 28}{space 2} .1715073{col 39}{space 1}   -0.01{col 48}{space 3}0.992{col 56}{space 4}-.3378008{col 69}{space 3} .3344953
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.4702066{col 28}{space 2} .2014213{col 39}{space 1}   -2.33{col 48}{space 3}0.020{col 56}{space 4} -.864985{col 69}{space 3}-.0754282
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} -.357822{col 28}{space 2} .3528518{col 39}{space 1}   -1.01{col 48}{space 3}0.311{col 56}{space 4}-1.049399{col 69}{space 3} .3337548
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Personal Victimization by ISIS (Logit Regression)
. 
. logit revdgsunnibias punishedisis female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-141.96094}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-141.38755}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-141.38603}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-141.38603}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-141.38603}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0721}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}punishedisis {c |}{col 16}{res}{space 2}-1.363898{col 28}{space 2} .1869547{col 39}{space 1}   -7.30{col 48}{space 3}0.000{col 56}{space 4}-1.730322{col 69}{space 3}-.9974735
{txt}{space 8}female {c |}{col 16}{res}{space 2} 1.020522{col 28}{space 2} .4660159{col 39}{space 1}    2.19{col 48}{space 3}0.029{col 56}{space 4} .1071477{col 69}{space 3} 1.933896
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0179657{col 28}{space 2} .0072212{col 39}{space 1}   -2.49{col 48}{space 3}0.013{col 56}{space 4} -.032119{col 69}{space 3}-.0038124
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.0529832{col 28}{space 2} .2152485{col 39}{space 1}   -0.25{col 48}{space 3}0.806{col 56}{space 4}-.4748626{col 69}{space 3} .3688961
{txt}{space 7}laborer {c |}{col 16}{res}{space 2} .0162962{col 28}{space 2} .1788928{col 39}{space 1}    0.09{col 48}{space 3}0.927{col 56}{space 4}-.3343274{col 69}{space 3} .3669197
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .2168301{col 28}{space 2} .1070551{col 39}{space 1}    2.03{col 48}{space 3}0.043{col 56}{space 4}  .007006{col 69}{space 3} .4266541
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5692277{col 28}{space 2} .2619288{col 39}{space 1}   -2.17{col 48}{space 3}0.030{col 56}{space 4}-1.082599{col 69}{space 3}-.0558568
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.3931532{col 28}{space 2} .5380399{col 39}{space 1}   -0.73{col 48}{space 3}0.465{col 56}{space 4}-1.447692{col 69}{space 3} .6613856
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias injuredisis female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.77054}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.57634}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.57623}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.57623}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.57623}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0446}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}injuredisis {c |}{col 16}{res}{space 2} .7851278{col 28}{space 2}  .960028{col 39}{space 1}    0.82{col 48}{space 3}0.413{col 56}{space 4}-1.096493{col 69}{space 3} 2.666748
{txt}{space 8}female {c |}{col 16}{res}{space 2} .9309854{col 28}{space 2} .3830246{col 39}{space 1}    2.43{col 48}{space 3}0.015{col 56}{space 4} .1802711{col 69}{space 3}   1.6817
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0126647{col 28}{space 2} .0072043{col 39}{space 1}   -1.76{col 48}{space 3}0.079{col 56}{space 4}-.0267848{col 69}{space 3} .0014555
{txt}{space 2}professional {c |}{col 16}{res}{space 2} -.254397{col 28}{space 2}   .24556{col 39}{space 1}   -1.04{col 48}{space 3}0.300{col 56}{space 4}-.7356857{col 69}{space 3} .2268917
{txt}{space 7}laborer {c |}{col 16}{res}{space 2} -.105798{col 28}{space 2} .2514609{col 39}{space 1}   -0.42{col 48}{space 3}0.674{col 56}{space 4}-.5986523{col 69}{space 3} .3870562
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0571825{col 28}{space 2} .1949765{col 39}{space 1}    0.29{col 48}{space 3}0.769{col 56}{space 4}-.3249645{col 69}{space 3} .4393295
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5509409{col 28}{space 2} .2161036{col 39}{space 1}   -2.55{col 48}{space 3}0.011{col 56}{space 4}-.9744963{col 69}{space 3}-.1273856
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.6232058{col 28}{space 2} .5296895{col 39}{space 1}   -1.18{col 48}{space 3}0.239{col 56}{space 4}-1.661378{col 69}{space 3} .4149664
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias imprisonedisis female age professional laborer unemployed westmosul, cluster(location)

{txt}note: {bf:imprisonedisis} != 0 predicts failure perfectly;
      {bf:imprisonedisis} omitted and 4 obs not used.

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-151.27197}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-144.99104}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-144.82831}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-144.82821}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-144.82821}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:274}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-144.82821}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0426}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
imprisonedisis {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 8}female {c |}{col 16}{res}{space 2} .9500973{col 28}{space 2} .4527381{col 39}{space 1}    2.10{col 48}{space 3}0.036{col 56}{space 4} .0627469{col 69}{space 3} 1.837448
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0124867{col 28}{space 2} .0069234{col 39}{space 1}   -1.80{col 48}{space 3}0.071{col 56}{space 4}-.0260562{col 69}{space 3} .0010828
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.2574444{col 28}{space 2} .2170034{col 39}{space 1}   -1.19{col 48}{space 3}0.235{col 56}{space 4}-.6827633{col 69}{space 3} .1678746
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.0152213{col 28}{space 2}  .273556{col 39}{space 1}   -0.06{col 48}{space 3}0.956{col 56}{space 4}-.5513812{col 69}{space 3} .5209385
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0271017{col 28}{space 2} .1412348{col 39}{space 1}    0.19{col 48}{space 3}0.848{col 56}{space 4}-.2497133{col 69}{space 3} .3039168
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5738984{col 28}{space 2} .2223554{col 39}{space 1}   -2.58{col 48}{space 3}0.010{col 56}{space 4}-1.009707{col 69}{space 3}-.1380898
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.5963504{col 28}{space 2} .4685771{col 39}{space 1}   -1.27{col 48}{space 3}0.203{col 56}{space 4}-1.514745{col 69}{space 3} .3220438
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias dsexassault female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.36991}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.14459}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.14407}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.14407}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.14407}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0474}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}dsexassault {c |}{col 16}{res}{space 2}-.8587162{col 28}{space 2} .2325741{col 39}{space 1}   -3.69{col 48}{space 3}0.000{col 56}{space 4}-1.314553{col 69}{space 3}-.4028793
{txt}{space 8}female {c |}{col 16}{res}{space 2} .9673279{col 28}{space 2} .4513047{col 39}{space 1}    2.14{col 48}{space 3}0.032{col 56}{space 4} .0827869{col 69}{space 3} 1.851869
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0139334{col 28}{space 2} .0063854{col 39}{space 1}   -2.18{col 48}{space 3}0.029{col 56}{space 4}-.0264486{col 69}{space 3}-.0014183
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.2957139{col 28}{space 2} .2144476{col 39}{space 1}   -1.38{col 48}{space 3}0.168{col 56}{space 4}-.7160234{col 69}{space 3} .1245956
{txt}{space 7}laborer {c |}{col 16}{res}{space 2} -.133938{col 28}{space 2} .2414915{col 39}{space 1}   -0.55{col 48}{space 3}0.579{col 56}{space 4}-.6072526{col 69}{space 3} .3393766
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0008763{col 28}{space 2} .1379594{col 39}{space 1}    0.01{col 48}{space 3}0.995{col 56}{space 4}-.2695192{col 69}{space 3} .2712717
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5281268{col 28}{space 2} .1838194{col 39}{space 1}   -2.87{col 48}{space 3}0.004{col 56}{space 4}-.8884062{col 69}{space 3}-.1678474
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} -.512335{col 28}{space 2} .4359306{col 39}{space 1}   -1.18{col 48}{space 3}0.240{col 56}{space 4}-1.366743{col 69}{space 3} .3420733
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Kinship Victimization (Logit Regression)
. 
. logit revdgsunnibias fampunishedisis female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-143.38288}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-143.05333}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-143.05312}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-143.05312}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-143.05312}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0611}

{txt}{ralign 81:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revdgsunnibias{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
fampunishedisis {c |}{col 17}{res}{space 2} 1.705158{col 29}{space 2} .3594603{col 40}{space 1}    4.74{col 49}{space 3}0.000{col 57}{space 4} 1.000629{col 70}{space 3} 2.409687
{txt}{space 9}female {c |}{col 17}{res}{space 2} 1.012742{col 29}{space 2}  .422731{col 40}{space 1}    2.40{col 49}{space 3}0.017{col 57}{space 4} .1842048{col 70}{space 3}  1.84128
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0140352{col 29}{space 2} .0068581{col 40}{space 1}   -2.05{col 49}{space 3}0.041{col 57}{space 4}-.0274768{col 70}{space 3}-.0005936
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-.3141241{col 29}{space 2} .2407854{col 40}{space 1}   -1.30{col 49}{space 3}0.192{col 57}{space 4}-.7860547{col 70}{space 3} .1578065
{txt}{space 8}laborer {c |}{col 17}{res}{space 2}-.8033927{col 29}{space 2} .2260063{col 40}{space 1}   -3.55{col 49}{space 3}0.000{col 57}{space 4}-1.246357{col 70}{space 3}-.3604284
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} .0231801{col 29}{space 2} .1452486{col 40}{space 1}    0.16{col 49}{space 3}0.873{col 57}{space 4}-.2615019{col 70}{space 3} .3078622
{txt}{space 6}westmosul {c |}{col 17}{res}{space 2}-.4612984{col 29}{space 2} .1976281{col 40}{space 1}   -2.33{col 49}{space 3}0.020{col 57}{space 4}-.8486423{col 70}{space 3}-.0739545
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.6061561{col 29}{space 2} .4873448{col 40}{space 1}   -1.24{col 49}{space 3}0.214{col 57}{space 4}-1.561334{col 70}{space 3} .3490221
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias faminjuredisis female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-144.32115}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-144.07281}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-144.07269}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-144.07269}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-144.07269}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0544}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
faminjuredisis {c |}{col 16}{res}{space 2} .9889925{col 28}{space 2}  .300693{col 39}{space 1}    3.29{col 48}{space 3}0.001{col 56}{space 4}  .399645{col 69}{space 3}  1.57834
{txt}{space 8}female {c |}{col 16}{res}{space 2} 1.067823{col 28}{space 2} .4102188{col 39}{space 1}    2.60{col 48}{space 3}0.009{col 56}{space 4}  .263809{col 69}{space 3} 1.871837
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0102005{col 28}{space 2}  .007123{col 39}{space 1}   -1.43{col 48}{space 3}0.152{col 56}{space 4}-.0241613{col 69}{space 3} .0037602
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.3167233{col 28}{space 2} .2172442{col 39}{space 1}   -1.46{col 48}{space 3}0.145{col 56}{space 4}-.7425141{col 69}{space 3} .1090675
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.1262668{col 28}{space 2} .2549408{col 39}{space 1}   -0.50{col 48}{space 3}0.620{col 56}{space 4}-.6259416{col 69}{space 3}  .373408
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0169489{col 28}{space 2} .1311045{col 39}{space 1}    0.13{col 48}{space 3}0.897{col 56}{space 4}-.2400113{col 69}{space 3}  .273909
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.4762358{col 28}{space 2} .1605934{col 39}{space 1}   -2.97{col 48}{space 3}0.003{col 56}{space 4} -.790993{col 69}{space 3}-.1614785
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  -.78485{col 28}{space 2} .4525198{col 39}{space 1}   -1.73{col 48}{space 3}0.083{col 56}{space 4}-1.671773{col 69}{space 3} .1020726
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Proprietary Victimization (Logit Regression)
. 
. logit revdgsunnibias fleehomeisis female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-143.15784}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-142.81235}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-142.81221}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-142.81221}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-142.81221}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0627}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}fleehomeisis {c |}{col 16}{res}{space 2} 1.101203{col 28}{space 2} .4844088{col 39}{space 1}    2.27{col 48}{space 3}0.023{col 56}{space 4} .1517788{col 69}{space 3} 2.050626
{txt}{space 8}female {c |}{col 16}{res}{space 2} 1.000229{col 28}{space 2} .4121457{col 39}{space 1}    2.43{col 48}{space 3}0.015{col 56}{space 4} .1924383{col 69}{space 3}  1.80802
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0114331{col 28}{space 2} .0051119{col 39}{space 1}   -2.24{col 48}{space 3}0.025{col 56}{space 4}-.0214523{col 69}{space 3}-.0014139
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.1133456{col 28}{space 2} .1979035{col 39}{space 1}   -0.57{col 48}{space 3}0.567{col 56}{space 4}-.5012293{col 69}{space 3} .2745381
{txt}{space 7}laborer {c |}{col 16}{res}{space 2} .0298141{col 28}{space 2} .2193789{col 39}{space 1}    0.14{col 48}{space 3}0.892{col 56}{space 4}-.4001607{col 69}{space 3} .4597889
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0892816{col 28}{space 2}  .059639{col 39}{space 1}    1.50{col 48}{space 3}0.134{col 56}{space 4}-.0276086{col 69}{space 3} .2061719
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2} -.410162{col 28}{space 2} .1984865{col 39}{space 1}   -2.07{col 48}{space 3}0.039{col 56}{space 4}-.7991884{col 69}{space 3}-.0211355
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.9284716{col 28}{space 2} .4119492{col 39}{space 1}   -2.25{col 48}{space 3}0.024{col 56}{space 4}-1.735877{col 69}{space 3} -.121066
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias homedamagedisis female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.39029}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.19514}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.19501}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.19501}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.19501}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0471}

{txt}{ralign 81:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revdgsunnibias{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
homedamagedisis {c |}{col 17}{res}{space 2} .4103864{col 29}{space 2} .1162572{col 40}{space 1}    3.53{col 49}{space 3}0.000{col 57}{space 4} .1825266{col 70}{space 3} .6382463
{txt}{space 9}female {c |}{col 17}{res}{space 2} .9361124{col 29}{space 2} .4785316{col 40}{space 1}    1.96{col 49}{space 3}0.050{col 57}{space 4}-.0017923{col 70}{space 3} 1.874017
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0126447{col 29}{space 2} .0069228{col 40}{space 1}   -1.83{col 49}{space 3}0.068{col 57}{space 4}-.0262132{col 70}{space 3} .0009238
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-.2946539{col 29}{space 2}  .249224{col 40}{space 1}   -1.18{col 49}{space 3}0.237{col 57}{space 4}-.7831239{col 70}{space 3} .1938162
{txt}{space 8}laborer {c |}{col 17}{res}{space 2}-.0620944{col 29}{space 2} .2507316{col 40}{space 1}   -0.25{col 49}{space 3}0.804{col 57}{space 4}-.5535193{col 70}{space 3} .4293305
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0637633{col 29}{space 2} .1608406{col 40}{space 1}   -0.40{col 49}{space 3}0.692{col 57}{space 4}-.3790052{col 70}{space 3} .2514785
{txt}{space 6}westmosul {c |}{col 17}{res}{space 2}-.5644952{col 29}{space 2} .2415947{col 40}{space 1}   -2.34{col 49}{space 3}0.019{col 57}{space 4}-1.038012{col 70}{space 3}-.0909783
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.6650581{col 29}{space 2} .5067206{col 40}{space 1}   -1.31{col 49}{space 3}0.189{col 57}{space 4}-1.658212{col 70}{space 3}  .328096
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias fledhomelib female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-137.34849}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-136.72216}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-136.71459}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-136.71459}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-136.71459}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1027}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}fledhomelib {c |}{col 16}{res}{space 2} 2.068525{col 28}{space 2}  .455532{col 39}{space 1}    4.54{col 48}{space 3}0.000{col 56}{space 4} 1.175698{col 69}{space 3} 2.961351
{txt}{space 8}female {c |}{col 16}{res}{space 2} 1.002291{col 28}{space 2} .4021788{col 39}{space 1}    2.49{col 48}{space 3}0.013{col 56}{space 4} .2140352{col 69}{space 3} 1.790547
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0046353{col 28}{space 2} .0080244{col 39}{space 1}   -0.58{col 48}{space 3}0.563{col 56}{space 4}-.0203628{col 69}{space 3} .0110922
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.2365873{col 28}{space 2} .1780042{col 39}{space 1}   -1.33{col 48}{space 3}0.184{col 56}{space 4}-.5854691{col 69}{space 3} .1122944
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.0394553{col 28}{space 2} .3076796{col 39}{space 1}   -0.13{col 48}{space 3}0.898{col 56}{space 4}-.6424961{col 69}{space 3} .5635856
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0908545{col 28}{space 2} .3204424{col 39}{space 1}    0.28{col 48}{space 3}0.777{col 56}{space 4}-.5372011{col 69}{space 3} .7189101
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.4325832{col 28}{space 2} .1552175{col 39}{space 1}   -2.79{col 48}{space 3}0.005{col 56}{space 4} -.736804{col 69}{space 3}-.1283624
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-1.157472{col 28}{space 2} .4437141{col 39}{space 1}   -2.61{col 48}{space 3}0.009{col 56}{space 4}-2.027135{col 69}{space 3}-.2878083
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias homedamagedlib female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.57615}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.37118}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.37108}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.37108}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.37108}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0459}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
homedamagedlib {c |}{col 16}{res}{space 2} .6193895{col 28}{space 2} .7521585{col 39}{space 1}    0.82{col 48}{space 3}0.410{col 56}{space 4}-.8548141{col 69}{space 3} 2.093593
{txt}{space 8}female {c |}{col 16}{res}{space 2} .8933415{col 28}{space 2}  .378136{col 39}{space 1}    2.36{col 48}{space 3}0.018{col 56}{space 4} .1522085{col 69}{space 3} 1.634474
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0124643{col 28}{space 2} .0071975{col 39}{space 1}   -1.73{col 48}{space 3}0.083{col 56}{space 4}-.0265711{col 69}{space 3} .0016425
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.2625453{col 28}{space 2} .2210699{col 39}{space 1}   -1.19{col 48}{space 3}0.235{col 56}{space 4}-.6958345{col 69}{space 3} .1707438
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.1968428{col 28}{space 2} .2452444{col 39}{space 1}   -0.80{col 48}{space 3}0.422{col 56}{space 4}-.6775129{col 69}{space 3} .2838273
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} -.098042{col 28}{space 2}  .119518{col 39}{space 1}   -0.82{col 48}{space 3}0.412{col 56}{space 4}-.3322929{col 69}{space 3}  .136209
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5741057{col 28}{space 2} .2137606{col 39}{space 1}   -2.69{col 48}{space 3}0.007{col 56}{space 4}-.9930688{col 69}{space 3}-.1551426
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.6079334{col 28}{space 2} .4964563{col 39}{space 1}   -1.22{col 48}{space 3}0.221{col 56}{space 4} -1.58097{col 69}{space 3}  .365103
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias homelootedlib female age professional laborer unemployed westmosul, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.60565}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.41599}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.41586}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.41586}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.41586}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0456}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}homelootedlib {c |}{col 16}{res}{space 2} .3327605{col 28}{space 2} .1369946{col 39}{space 1}    2.43{col 48}{space 3}0.015{col 56}{space 4} .0642561{col 69}{space 3} .6012649
{txt}{space 8}female {c |}{col 16}{res}{space 2} .9970792{col 28}{space 2} .4436792{col 39}{space 1}    2.25{col 48}{space 3}0.025{col 56}{space 4}  .127484{col 69}{space 3} 1.866674
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0138353{col 28}{space 2} .0078738{col 39}{space 1}   -1.76{col 48}{space 3}0.079{col 56}{space 4}-.0292677{col 69}{space 3}  .001597
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.2813909{col 28}{space 2} .2428503{col 39}{space 1}   -1.16{col 48}{space 3}0.247{col 56}{space 4}-.7573686{col 69}{space 3} .1945869
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.0906587{col 28}{space 2} .2377225{col 39}{space 1}   -0.38{col 48}{space 3}0.703{col 56}{space 4}-.5565862{col 69}{space 3} .3752688
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0008284{col 28}{space 2} .1595203{col 39}{space 1}    0.01{col 48}{space 3}0.996{col 56}{space 4}-.3118257{col 69}{space 3} .3134825
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5540005{col 28}{space 2} .2269266{col 39}{space 1}   -2.44{col 48}{space 3}0.015{col 56}{space 4}-.9987684{col 69}{space 3}-.1092326
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.6337971{col 28}{space 2} .5001185{col 39}{space 1}   -1.27{col 48}{space 3}0.205{col 56}{space 4}-1.614011{col 69}{space 3} .3464171
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Pre-ISIS Victimization (Logit Regression)
. 
. logit revdgsunnibias iraniraq female age professional laborer unemployed westmosul if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-142.94016}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-142.47313}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-142.47147}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-142.47147}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-142.47147}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0649}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}iraniraq {c |}{col 16}{res}{space 2}-1.344771{col 28}{space 2} .4811078{col 39}{space 1}   -2.80{col 48}{space 3}0.005{col 56}{space 4}-2.287725{col 69}{space 3}-.4018176
{txt}{space 8}female {c |}{col 16}{res}{space 2} .9655207{col 28}{space 2} .4327251{col 39}{space 1}    2.23{col 48}{space 3}0.026{col 56}{space 4}  .117395{col 69}{space 3} 1.813646
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0021513{col 28}{space 2} .0043522{col 39}{space 1}   -0.49{col 48}{space 3}0.621{col 56}{space 4}-.0106814{col 69}{space 3} .0063788
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.4509786{col 28}{space 2} .2201024{col 39}{space 1}   -2.05{col 48}{space 3}0.040{col 56}{space 4}-.8823714{col 69}{space 3}-.0195859
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.1693789{col 28}{space 2} .2736418{col 39}{space 1}   -0.62{col 48}{space 3}0.536{col 56}{space 4}-.7057069{col 69}{space 3} .3669491
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2}  .080112{col 28}{space 2} .1040744{col 39}{space 1}    0.77{col 48}{space 3}0.441{col 56}{space 4}  -.12387{col 69}{space 3}  .284094
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.6416795{col 28}{space 2} .2328784{col 39}{space 1}   -2.76{col 48}{space 3}0.006{col 56}{space 4}-1.098113{col 69}{space 3}-.1852462
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.6827384{col 28}{space 2} .4836126{col 39}{space 1}   -1.41{col 48}{space 3}0.158{col 56}{space 4}-1.630602{col 69}{space 3} .2651249
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias gulfwar  female age professional laborer unemployed westmosul if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:  -144.533}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-144.24648}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-144.24585}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-144.24585}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-144.24585}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0533}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}gulfwar {c |}{col 16}{res}{space 2} 1.684195{col 28}{space 2} 1.265744{col 39}{space 1}    1.33{col 48}{space 3}0.183{col 56}{space 4}-.7966177{col 69}{space 3} 4.165007
{txt}{space 8}female {c |}{col 16}{res}{space 2} 1.013981{col 28}{space 2} .4644232{col 39}{space 1}    2.18{col 48}{space 3}0.029{col 56}{space 4} .1037282{col 69}{space 3} 1.924234
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0126095{col 28}{space 2} .0057182{col 39}{space 1}   -2.21{col 48}{space 3}0.027{col 56}{space 4}-.0238169{col 69}{space 3}-.0014021
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.2178079{col 28}{space 2} .2763991{col 39}{space 1}   -0.79{col 48}{space 3}0.431{col 56}{space 4}-.7595402{col 69}{space 3} .3239244
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.0293294{col 28}{space 2} .1721755{col 39}{space 1}   -0.17{col 48}{space 3}0.865{col 56}{space 4}-.3667872{col 69}{space 3} .3081284
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .1070847{col 28}{space 2} .1734868{col 39}{space 1}    0.62{col 48}{space 3}0.537{col 56}{space 4}-.2329432{col 69}{space 3} .4471126
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5278068{col 28}{space 2} .2200443{col 39}{space 1}   -2.40{col 48}{space 3}0.016{col 56}{space 4}-.9590856{col 69}{space 3}-.0965279
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.7054179{col 28}{space 2} .5248362{col 39}{space 1}   -1.34{col 48}{space 3}0.179{col 56}{space 4}-1.734078{col 69}{space 3} .3232422
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias saddampre90 female age professional laborer unemployed westmosul  if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-137.09706}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-136.65735}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-136.65142}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-136.65142}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-136.65142}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1031}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}saddampre90 {c |}{col 16}{res}{space 2} 3.170343{col 28}{space 2}  .564067{col 39}{space 1}    5.62{col 48}{space 3}0.000{col 56}{space 4} 2.064792{col 69}{space 3} 4.275894
{txt}{space 8}female {c |}{col 16}{res}{space 2} 1.019522{col 28}{space 2} .5299028{col 39}{space 1}    1.92{col 48}{space 3}0.054{col 56}{space 4}-.0190682{col 69}{space 3} 2.058113
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0176787{col 28}{space 2} .0063315{col 39}{space 1}   -2.79{col 48}{space 3}0.005{col 56}{space 4}-.0300882{col 69}{space 3}-.0052692
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.5211424{col 28}{space 2} .2606454{col 39}{space 1}   -2.00{col 48}{space 3}0.046{col 56}{space 4}-1.031998{col 69}{space 3}-.0102868
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.1173243{col 28}{space 2} .2458992{col 39}{space 1}   -0.48{col 48}{space 3}0.633{col 56}{space 4}-.5992779{col 69}{space 3} .3646293
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0020011{col 28}{space 2} .1420743{col 39}{space 1}    0.01{col 48}{space 3}0.989{col 56}{space 4}-.2764595{col 69}{space 3} .2804617
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2} -.576508{col 28}{space 2} .2550085{col 39}{space 1}   -2.26{col 48}{space 3}0.024{col 56}{space 4}-1.076315{col 69}{space 3}-.0767005
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.4645219{col 28}{space 2} .4717279{col 39}{space 1}   -0.98{col 48}{space 3}0.325{col 56}{space 4}-1.389092{col 69}{space 3} .4600477
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias iraqwar female age professional laborer unemployed westmosul if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.32469}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.11511}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.11499}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.11499}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.11499}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0476}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}iraqwar {c |}{col 16}{res}{space 2} .9079744{col 28}{space 2} .6850615{col 39}{space 1}    1.33{col 48}{space 3}0.185{col 56}{space 4}-.4347215{col 69}{space 3}  2.25067
{txt}{space 8}female {c |}{col 16}{res}{space 2} .9035505{col 28}{space 2} .4142831{col 39}{space 1}    2.18{col 48}{space 3}0.029{col 56}{space 4} .0915705{col 69}{space 3}  1.71553
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0132447{col 28}{space 2} .0068759{col 39}{space 1}   -1.93{col 48}{space 3}0.054{col 56}{space 4}-.0267212{col 69}{space 3} .0002319
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.3179787{col 28}{space 2} .2643018{col 39}{space 1}   -1.20{col 48}{space 3}0.229{col 56}{space 4}-.8360007{col 69}{space 3} .2000432
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.1427908{col 28}{space 2} .2333684{col 39}{space 1}   -0.61{col 48}{space 3}0.541{col 56}{space 4}-.6001846{col 69}{space 3} .3146029
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2}-.0465106{col 28}{space 2} .1187805{col 39}{space 1}   -0.39{col 48}{space 3}0.695{col 56}{space 4} -.279316{col 69}{space 3} .1862949
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5790433{col 28}{space 2} .2431336{col 39}{space 1}   -2.38{col 48}{space 3}0.017{col 56}{space 4}-1.055576{col 69}{space 3}-.1025103
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.5646924{col 28}{space 2} .4667291{col 39}{space 1}   -1.21{col 48}{space 3}0.226{col 56}{space 4}-1.479465{col 69}{space 3} .3500799
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias insurgency03 female age professional laborer unemployed westmosul if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-141.92299}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-141.60298}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -141.6026}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -141.6026}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-141.6026}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0706}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}insurgency03 {c |}{col 16}{res}{space 2} 1.030045{col 28}{space 2} .2351162{col 39}{space 1}    4.38{col 48}{space 3}0.000{col 56}{space 4} .5692254{col 69}{space 3} 1.490864
{txt}{space 8}female {c |}{col 16}{res}{space 2} 1.121181{col 28}{space 2} .4868907{col 39}{space 1}    2.30{col 48}{space 3}0.021{col 56}{space 4} .1668924{col 69}{space 3} 2.075469
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0102207{col 28}{space 2} .0065839{col 39}{space 1}   -1.55{col 48}{space 3}0.121{col 56}{space 4}-.0231249{col 69}{space 3} .0026835
{txt}{space 2}professional {c |}{col 16}{res}{space 2} -.430817{col 28}{space 2} .1869183{col 39}{space 1}   -2.30{col 48}{space 3}0.021{col 56}{space 4}-.7971701{col 69}{space 3} -.064464
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.4892581{col 28}{space 2} .2488051{col 39}{space 1}   -1.97{col 48}{space 3}0.049{col 56}{space 4}-.9769072{col 69}{space 3} -.001609
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2}-.1455115{col 28}{space 2} .0535333{col 39}{space 1}   -2.72{col 48}{space 3}0.007{col 56}{space 4}-.2504349{col 69}{space 3}-.0405881
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.5656073{col 28}{space 2} .2121291{col 39}{space 1}   -2.67{col 48}{space 3}0.008{col 56}{space 4}-.9813728{col 69}{space 3}-.1498418
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.8217098{col 28}{space 2} .4669989{col 39}{space 1}   -1.76{col 48}{space 3}0.078{col 56}{space 4}-1.737011{col 69}{space 3} .0935912
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias crime03 female age professional laborer unemployed westmosul if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-139.41045}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-138.77252}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-138.77092}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-138.77092}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-138.77092}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0892}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}crime03 {c |}{col 16}{res}{space 2}-1.359214{col 28}{space 2} .1535272{col 39}{space 1}   -8.85{col 48}{space 3}0.000{col 56}{space 4}-1.660122{col 69}{space 3}-1.058306
{txt}{space 8}female {c |}{col 16}{res}{space 2} .9720547{col 28}{space 2} .4840367{col 39}{space 1}    2.01{col 48}{space 3}0.045{col 56}{space 4} .0233601{col 69}{space 3} 1.920749
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0158033{col 28}{space 2} .0072671{col 39}{space 1}   -2.17{col 48}{space 3}0.030{col 56}{space 4}-.0300465{col 69}{space 3}-.0015602
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.3300058{col 28}{space 2} .1974071{col 39}{space 1}   -1.67{col 48}{space 3}0.095{col 56}{space 4}-.7169166{col 69}{space 3} .0569049
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.4217055{col 28}{space 2} .1249388{col 39}{space 1}   -3.38{col 48}{space 3}0.001{col 56}{space 4}-.6665809{col 69}{space 3}  -.17683
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} -.009517{col 28}{space 2} .1705677{col 39}{space 1}   -0.06{col 48}{space 3}0.956{col 56}{space 4}-.3438235{col 69}{space 3} .3247896
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.4265161{col 28}{space 2} .1897882{col 39}{space 1}   -2.25{col 48}{space 3}0.025{col 56}{space 4}-.7984941{col 69}{space 3}-.0545381
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.1452254{col 28}{space 2} .4500908{col 39}{space 1}   -0.32{col 48}{space 3}0.747{col 56}{space 4}-1.027387{col 69}{space 3} .7369363
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Covid-related Victimization (Logit Regression)
. 
. logit revdgsunnibias hadcovid female age professional laborer unemployed westmosul if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.76806}  
Iteration 2:{space 2}Log pseudolikelihood = {res: -145.5859}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.58579}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.58579}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.58579}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0445}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}hadcovid {c |}{col 16}{res}{space 2}-.2159482{col 28}{space 2} .4103263{col 39}{space 1}   -0.53{col 48}{space 3}0.599{col 56}{space 4}-1.020173{col 69}{space 3} .5882766
{txt}{space 8}female {c |}{col 16}{res}{space 2} .9899188{col 28}{space 2} .4135341{col 39}{space 1}    2.39{col 48}{space 3}0.017{col 56}{space 4} .1794068{col 69}{space 3} 1.800431
{txt}{space 11}age {c |}{col 16}{res}{space 2} -.012879{col 28}{space 2} .0061449{col 39}{space 1}   -2.10{col 48}{space 3}0.036{col 56}{space 4}-.0249228{col 69}{space 3}-.0008353
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.2150471{col 28}{space 2} .1805474{col 39}{space 1}   -1.19{col 48}{space 3}0.234{col 56}{space 4}-.5689135{col 69}{space 3} .1388193
{txt}{space 7}laborer {c |}{col 16}{res}{space 2}-.0738898{col 28}{space 2} .2174376{col 39}{space 1}   -0.34{col 48}{space 3}0.734{col 56}{space 4}-.5000597{col 69}{space 3} .3522802
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0826451{col 28}{space 2} .0504213{col 39}{space 1}    1.64{col 48}{space 3}0.101{col 56}{space 4}-.0161788{col 69}{space 3}  .181469
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2} -.558907{col 28}{space 2} .2361393{col 39}{space 1}   -2.37{col 48}{space 3}0.018{col 56}{space 4}-1.021731{col 69}{space 3}-.0960825
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.5344293{col 28}{space 2} .5917225{col 39}{space 1}   -0.90{col 48}{space 3}0.366{col 56}{space 4}-1.694184{col 69}{space 3} .6253255
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias diedcovid female age professional laborer unemployed westmosul  if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -144.1929}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-143.92215}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-143.92165}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-143.92165}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-143.92165}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0554}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}diedcovid {c |}{col 16}{res}{space 2}-.6668284{col 28}{space 2} .4074811{col 39}{space 1}   -1.64{col 48}{space 3}0.102{col 56}{space 4}-1.465477{col 69}{space 3} .1318198
{txt}{space 8}female {c |}{col 16}{res}{space 2} 1.126202{col 28}{space 2} .3773474{col 39}{space 1}    2.98{col 48}{space 3}0.003{col 56}{space 4} .3866149{col 69}{space 3}  1.86579
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0135365{col 28}{space 2} .0061753{col 39}{space 1}   -2.19{col 48}{space 3}0.028{col 56}{space 4}-.0256398{col 69}{space 3}-.0014331
{txt}{space 2}professional {c |}{col 16}{res}{space 2}-.1559994{col 28}{space 2} .1844969{col 39}{space 1}   -0.85{col 48}{space 3}0.398{col 56}{space 4}-.5176067{col 69}{space 3} .2056078
{txt}{space 7}laborer {c |}{col 16}{res}{space 2} .0537853{col 28}{space 2} .1870164{col 39}{space 1}    0.29{col 48}{space 3}0.774{col 56}{space 4}-.3127601{col 69}{space 3} .4203306
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2} .0552336{col 28}{space 2} .1685896{col 39}{space 1}    0.33{col 48}{space 3}0.743{col 56}{space 4}-.2751961{col 69}{space 3} .3856632
{txt}{space 5}westmosul {c |}{col 16}{res}{space 2}-.6118646{col 28}{space 2} .2789824{col 39}{space 1}   -2.19{col 48}{space 3}0.028{col 56}{space 4} -1.15866{col 69}{space 3}-.0650692
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} -.473617{col 28}{space 2} .6049353{col 39}{space 1}   -0.78{col 48}{space 3}0.434{col 56}{space 4}-1.659268{col 69}{space 3} .7120343
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Demographic Correlates
. 
. pwcorr  revdgsunnibias closeoutgroup female age professional laborer unemployed westmosul  if mosul==1, sig

             {txt}{c |} revdgsu~ closeo~p   female      age profes~l  laborer unempl~d
{hline 13}{c +}{hline 63}
revdgsunni~s {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
closeoutgr~p {c |} {res}  0.2939   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
      female {c |} {res}  0.1659   0.1137   1.0000 
             {txt}{c |}{res}   0.0055   0.0584
             {txt}{c |}
         age {c |} {res} -0.0893  -0.1761   0.0142   1.0000 
             {txt}{c |}{res}   0.1376   0.0032   0.8130
             {txt}{c |}
professional {c |} {res} -0.0660   0.0271   0.0360   0.3482   1.0000 
             {txt}{c |}{res}   0.2725   0.6532   0.5495   0.0000
             {txt}{c |}
     laborer {c |} {res} -0.0050   0.0580  -0.1355  -0.1221  -0.3403   1.0000 
             {txt}{c |}{res}   0.9337   0.3356   0.0238   0.0420   0.0000
             {txt}{c |}
  unemployed {c |} {res}  0.0585  -0.0168   0.0861  -0.2376  -0.3615  -0.1096   1.0000 
             {txt}{c |}{res}   0.3312   0.7798   0.1524   0.0001   0.0000   0.0680
             {txt}{c |}
   westmosul {c |} {res} -0.1069  -0.1001  -0.0349   0.0467  -0.0346  -0.0796  -0.0029 
             {txt}{c |}{res}   0.0751   0.0959   0.5626   0.4377   0.5652   0.1856   0.9612
             {txt}{c |}

             {c |} westmo~l
{hline 13}{c +}{hline 9}
   westmosul {c |} {res}  1.0000 
             {txt}{c |}
             {c |}

{com}. 
. *Manuscript Table 2 Robustness Checks
. 
. *Table 2. Victimization and Out-group Altruism (Logit Regression)
. 
. logit revdgsunnibias mmx_tetraisisvictim, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.01365}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-144.88816}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-144.88814}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-144.88814}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:94.04}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-144.88814}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0491}

{txt}{ralign 88:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        revdgsunnibias{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_tetraisisvictimall {c |}{col 24}{res}{space 2} 3.426901{col 36}{space 2} .3533858{col 47}{space 1}    9.70{col 56}{space 3}0.000{col 64}{space 4} 2.734278{col 77}{space 3} 4.119524
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}-2.424147{col 36}{space 2} .1066529{col 47}{space 1}  -22.73{col 56}{space 3}0.000{col 64}{space 4}-2.633183{col 77}{space 3}-2.215112
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_tetrapreisisvictim, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-147.92127}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-147.84262}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-147.84261}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:67.41}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-147.84261}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0297}

{txt}{ralign 91:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}           revdgsunnibias{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_tetrapreisisvictimall {c |}{col 27}{res}{space 2} 1.056339{col 39}{space 2} .1286577{col 50}{space 1}    8.21{col 59}{space 3}0.000{col 67}{space 4} .8041747{col 80}{space 3} 1.308504
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}-1.527548{col 39}{space 2} .2229631{col 50}{space 1}   -6.85{col 59}{space 3}0.000{col 67}{space 4}-1.964547{col 80}{space 3}-1.090548
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias dsaworheardkilloutgroup if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-145.52069}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.05067}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.04879}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.04879}  
{res}
{txt}{col 1}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:278}
{txt}{col 56}{lalign 13:Wald chi2({res:1})}{col 69} = {res}{ralign 7:1189.55}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.04879}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0480}

{txt}{ralign 89:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         revdgsunnibias{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      z{col 57}   P>|z|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dsaworheardkilloutgroup {c |}{col 25}{res}{space 2} 1.693496{col 37}{space 2} .0491013{col 48}{space 1}   34.49{col 57}{space 3}0.000{col 65}{space 4} 1.597259{col 78}{space 3} 1.789733
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}-1.357024{col 37}{space 2} .1548351{col 48}{space 1}   -8.76{col 57}{space 3}0.000{col 65}{space 4}-1.660495{col 78}{space 3}-1.053553
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgsunnibias mmx_tetraisisvictim mmx_tetrapreisisvictim dsaworheardkilloutgroup hadcovid diedcovid female age professional laborer unemployed westmosul  if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-129.54463}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-128.17311}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-128.16604}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-128.16604}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-128.16604}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1588}

{txt}{ralign 91:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}           revdgsunnibias{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}mmx_tetraisisvictimall {c |}{col 27}{res}{space 2} 3.145674{col 39}{space 2} .4658875{col 50}{space 1}    6.75{col 59}{space 3}0.000{col 67}{space 4} 2.232551{col 80}{space 3} 4.058797
{txt}mmx_tetrapreisisvictimall {c |}{col 27}{res}{space 2} 1.410603{col 39}{space 2} .1426453{col 50}{space 1}    9.89{col 59}{space 3}0.000{col 67}{space 4} 1.131023{col 80}{space 3} 1.690183
{txt}{space 2}dsaworheardkilloutgroup {c |}{col 27}{res}{space 2} 1.349533{col 39}{space 2} .2526467{col 50}{space 1}    5.34{col 59}{space 3}0.000{col 67}{space 4} .8543541{col 80}{space 3} 1.844711
{txt}{space 17}hadcovid {c |}{col 27}{res}{space 2} .2501257{col 39}{space 2} .3064292{col 50}{space 1}    0.82{col 59}{space 3}0.414{col 67}{space 4}-.3504645{col 80}{space 3} .8507159
{txt}{space 16}diedcovid {c |}{col 27}{res}{space 2}-1.012559{col 39}{space 2}  .161737{col 50}{space 1}   -6.26{col 59}{space 3}0.000{col 67}{space 4}-1.329558{col 80}{space 3}-.6955603
{txt}{space 19}female {c |}{col 27}{res}{space 2}  1.07241{col 39}{space 2} .4124692{col 50}{space 1}    2.60{col 59}{space 3}0.009{col 67}{space 4} .2639856{col 80}{space 3} 1.880835
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0085537{col 39}{space 2} .0077067{col 50}{space 1}   -1.11{col 59}{space 3}0.267{col 67}{space 4}-.0236585{col 80}{space 3} .0065511
{txt}{space 13}professional {c |}{col 27}{res}{space 2}-.2081477{col 39}{space 2} .1457658{col 50}{space 1}   -1.43{col 59}{space 3}0.153{col 67}{space 4}-.4938435{col 80}{space 3} .0775481
{txt}{space 18}laborer {c |}{col 27}{res}{space 2}-.1438658{col 39}{space 2} .3874485{col 50}{space 1}   -0.37{col 59}{space 3}0.710{col 67}{space 4}-.9032509{col 80}{space 3} .6155193
{txt}{space 15}unemployed {c |}{col 27}{res}{space 2}-.2341884{col 39}{space 2} .1432823{col 50}{space 1}   -1.63{col 59}{space 3}0.102{col 67}{space 4}-.5150165{col 80}{space 3} .0466398
{txt}{space 16}westmosul {c |}{col 27}{res}{space 2}-.4527717{col 39}{space 2}  .243219{col 50}{space 1}   -1.86{col 59}{space 3}0.063{col 67}{space 4}-.9294721{col 80}{space 3} .0239288
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} -2.41446{col 39}{space 2} .7091721{col 50}{space 1}   -3.40{col 59}{space 3}0.001{col 67}{space 4}-3.804412{col 80}{space 3}-1.024508
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Table 2. Victimization and Out-group Altruism (Logit Regression)
. 
. logit revdgsunnibias  dheardkilloutgroup dsawkilloutgroup if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-143.59817}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-143.12204}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-143.11662}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-143.11662}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(1)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-143.11662}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0607}

{txt}{ralign 84:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    revdgsunnibias{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dheardkilloutgroup {c |}{col 20}{res}{space 2}  1.78259{col 32}{space 2} .0675238{col 43}{space 1}   26.40{col 52}{space 3}0.000{col 60}{space 4} 1.650246{col 73}{space 3} 1.914934
{txt}{space 2}dsawkilloutgroup {c |}{col 20}{res}{space 2} .9763182{col 32}{space 2} .1298004{col 43}{space 1}    7.52{col 52}{space 3}0.000{col 60}{space 4} .7219141{col 73}{space 3} 1.230722
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-1.376542{col 32}{space 2} .1654603{col 43}{space 1}   -8.32{col 52}{space 3}0.000{col 60}{space 4}-1.700839{col 73}{space 3}-1.052246
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. vif, uncentered

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
dheardkill~p {c |} {res}     1.14    0.878788
{txt}dsawkillou~p {c |} {res}     1.14    0.878788
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     1.14
{txt}
{com}. linktest

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-152.36517}  
Iteration 1:{space 2}Log likelihood = {res:-142.01125}  
Iteration 2:{space 2}Log likelihood = {res:-141.46033}  
Iteration 3:{space 2}Log likelihood = {res:-141.21492}  
Iteration 4:{space 2}Log likelihood = {res:-141.13629}  
Iteration 5:{space 2}Log likelihood = {res:-141.11882}  
Iteration 6:{space 2}Log likelihood = {res:-141.11456}  
Iteration 7:{space 2}Log likelihood = {res: -141.1137}  
Iteration 8:{space 2}Log likelihood = {res:-141.11352}  
Iteration 9:{space 2}Log likelihood = {res:-141.11348}  
Iteration 10:{space 1}Log likelihood = {res:-141.11347}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:LR chi2({res:2})}{col 70} = {res}{ralign 6:22.50}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-141.11347}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0738}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}_hat {c |}{col 16}{res}{space 2} 17.09827{col 28}{space 2} 799.0689{col 39}{space 1}    0.02{col 48}{space 3}0.983{col 56}{space 4}-1549.048{col 69}{space 3} 1583.245
{txt}{space 8}_hatsq {c |}{col 16}{res}{space 2} 16.70481{col 28}{space 2} 823.3625{col 39}{space 1}    0.02{col 48}{space 3}0.984{col 56}{space 4}-1597.056{col 69}{space 3} 1630.466
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-9.473963{col 28}{space 2} 460.2118{col 39}{space 1}   -0.02{col 48}{space 3}0.984{col 56}{space 4}-911.4725{col 69}{space 3} 892.5246
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 0 failures and 4 successes completely determined.{p_end}

{com}. lfit, group(10)
{txt}{p 0 6 2}note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: {res}revdgsunnibias

{txt}{ralign 23:Number of observations} = {res}{ralign 4:278}
{txt}{ralign 23:Number of groups} = {res}{ralign 4:2}
{txt}{ralign 23:Hosmer–Lemeshow chi2({res:0})} = {res}{ralign 4:0.13}
{txt}{ralign 23:Prob > chi2} = {res}{ralign 4:.}

{txt}{p 0 6 0}Warning: There are only 2 distinct quantiles because of ties.{p_end}

{com}. estat gof, group(10)
{txt}{p 0 6 2}note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: {res}revdgsunnibias

{txt}{ralign 23:Number of observations} = {res}{ralign 4:278}
{txt}{ralign 23:Number of groups} = {res}{ralign 4:2}
{txt}{ralign 23:Hosmer–Lemeshow chi2({res:0})} = {res}{ralign 4:0.13}
{txt}{ralign 23:Prob > chi2} = {res}{ralign 4:.}

{txt}{p 0 6 0}Warning: There are only 2 distinct quantiles because of ties.{p_end}

{com}. 
. logit revdgsunnibias  punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib  if mosul==1, cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-127.06783}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-125.78154}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-125.75514}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-125.75511}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-125.75511}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(2)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-125.75511}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1746}

{txt}{ralign 81:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revdgsunnibias{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}punishedisis {c |}{col 17}{res}{space 2}-.2461737{col 29}{space 2} 1.035391{col 40}{space 1}   -0.24{col 49}{space 3}0.812{col 57}{space 4}-2.275503{col 70}{space 3} 1.783156
{txt}{space 5}injuredlib {c |}{col 17}{res}{space 2} 1.462987{col 29}{space 2} 1.633418{col 40}{space 1}    0.90{col 49}{space 3}0.370{col 57}{space 4}-1.738453{col 70}{space 3} 4.664428
{txt}{space 2}imprisonedlib {c |}{col 17}{res}{space 2}-.1704658{col 29}{space 2} .6659114{col 40}{space 1}   -0.26{col 49}{space 3}0.798{col 57}{space 4}-1.475628{col 70}{space 3} 1.134697
{txt}{space 4}dsexassault {c |}{col 17}{res}{space 2}-.4789646{col 29}{space 2} .1001278{col 40}{space 1}   -4.78{col 49}{space 3}0.000{col 57}{space 4}-.6752114{col 70}{space 3}-.2827178
{txt}fampunishedisis {c |}{col 17}{res}{space 2} 2.065582{col 29}{space 2} .7084324{col 40}{space 1}    2.92{col 49}{space 3}0.004{col 57}{space 4} .6770796{col 70}{space 3} 3.454084
{txt}{space 1}faminjuredisis {c |}{col 17}{res}{space 2} .1062957{col 29}{space 2} .8200295{col 40}{space 1}    0.13{col 49}{space 3}0.897{col 57}{space 4}-1.500933{col 70}{space 3} 1.713524
{txt}{space 2}faminjuredlib {c |}{col 17}{res}{space 2} 2.396523{col 29}{space 2} .2445342{col 40}{space 1}    9.80{col 49}{space 3}0.000{col 57}{space 4} 1.917245{col 70}{space 3} 2.875802
{txt}womenabusedisis {c |}{col 17}{res}{space 2} .9659492{col 29}{space 2} .8205285{col 40}{space 1}    1.18{col 49}{space 3}0.239{col 57}{space 4}-.6422571{col 70}{space 3} 2.574156
{txt}{space 3}fleehomeisis {c |}{col 17}{res}{space 2} 1.583598{col 29}{space 2} .5694677{col 40}{space 1}    2.78{col 49}{space 3}0.005{col 57}{space 4} .4674621{col 70}{space 3} 2.699734
{txt}homedamagedisis {c |}{col 17}{res}{space 2} 1.379728{col 29}{space 2}  .846516{col 40}{space 1}    1.63{col 49}{space 3}0.103{col 57}{space 4}-.2794124{col 70}{space 3} 3.038869
{txt}{space 4}fledhomelib {c |}{col 17}{res}{space 2} 2.270794{col 29}{space 2} .5829478{col 40}{space 1}    3.90{col 49}{space 3}0.000{col 57}{space 4} 1.128237{col 70}{space 3} 3.413351
{txt}{space 1}homedamagedlib {c |}{col 17}{res}{space 2} .8111381{col 29}{space 2} .6479979{col 40}{space 1}    1.25{col 49}{space 3}0.211{col 57}{space 4}-.4589144{col 70}{space 3} 2.081191
{txt}{space 2}homelootedlib {c |}{col 17}{res}{space 2} .1711177{col 29}{space 2}  .393026{col 40}{space 1}    0.44{col 49}{space 3}0.663{col 57}{space 4}-.5991991{col 70}{space 3} .9414345
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-2.634332{col 29}{space 2} .8568182{col 40}{space 1}   -3.07{col 49}{space 3}0.002{col 57}{space 4}-4.313665{col 70}{space 3}-.9549991
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. vif, uncentered

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
homedamage~s {c |} {res}     2.87    0.348557
{txt}homelooted~b {c |} {res}     2.74    0.365295
{txt}faminjured~s {c |} {res}     2.73    0.366447
{txt}faminjured~b {c |} {res}     2.66    0.376297
{txt}imprisoned~b {c |} {res}     2.32    0.431070
{txt}punishedisis {c |} {res}     2.18    0.459052
{txt}fampunishe~s {c |} {res}     1.47    0.681273
{txt}fleehomeisis {c |} {res}     1.34    0.743850
{txt}{space 1}fledhomelib {c |} {res}     1.34    0.748986
{txt}homedamage~b {c |} {res}     1.33    0.754070
{txt}{space 2}injuredlib {c |} {res}     1.12    0.889906
{txt}{space 1}dsexassault {c |} {res}     1.11    0.898326
{txt}womenabuse~s {c |} {res}     1.04    0.962834
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     1.86
{txt}
{com}. linktest

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-152.36517}  
Iteration 1:{space 2}Log likelihood = {res:-126.59829}  
Iteration 2:{space 2}Log likelihood = {res:-125.73696}  
Iteration 3:{space 2}Log likelihood = {res:-125.71299}  
Iteration 4:{space 2}Log likelihood = {res:-125.71297}  
Iteration 5:{space 2}Log likelihood = {res:-125.71297}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:LR chi2({res:2})}{col 70} = {res}{ralign 6:53.30}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-125.71297}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1749}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}_hat {c |}{col 16}{res}{space 2} 1.035296{col 28}{space 2} .2088361{col 39}{space 1}    4.96{col 48}{space 3}0.000{col 56}{space 4} .6259847{col 69}{space 3} 1.444607
{txt}{space 8}_hatsq {c |}{col 16}{res}{space 2} .0287987{col 28}{space 2} .0990582{col 39}{space 1}    0.29{col 48}{space 3}0.771{col 56}{space 4}-.1653517{col 69}{space 3} .2229492
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0226883{col 28}{space 2} .2536226{col 39}{space 1}   -0.09{col 48}{space 3}0.929{col 56}{space 4}-.5197795{col 69}{space 3} .4744029
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lfit, group(10)
{txt}{p 0 6 2}note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: {res}revdgsunnibias

{txt}{ralign 23:Number of observations} = {res}{ralign 6:278}
{txt}{ralign 23:Number of groups} = {res}{ralign 6:8}
{txt}{ralign 23:Hosmer–Lemeshow chi2({res:6})} = {res}{ralign 6:4.21}
{txt}{ralign 23:Prob > chi2} = {res}{ralign 6:0.6490}

{txt}{p 0 6 0}Warning: There are only 8 distinct quantiles because of ties.{p_end}

{com}. estat gof, group(10)
{txt}{p 0 6 2}note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: {res}revdgsunnibias

{txt}{ralign 23:Number of observations} = {res}{ralign 6:278}
{txt}{ralign 23:Number of groups} = {res}{ralign 6:8}
{txt}{ralign 23:Hosmer–Lemeshow chi2({res:6})} = {res}{ralign 6:4.21}
{txt}{ralign 23:Prob > chi2} = {res}{ralign 6:0.6490}

{txt}{p 0 6 0}Warning: There are only 8 distinct quantiles because of ties.{p_end}

{com}. 
. logit revdgsunnibias  iraniraq-crime03 if mosul==1, cluster(location)

{txt}note: {bf:saddampost90} omitted because of collinearity.
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-133.51935}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-132.66029}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-132.64324}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-132.64323}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-132.64323}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(2)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-132.64323}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1294}

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}iraniraq {c |}{col 16}{res}{space 2}-1.196235{col 28}{space 2} .4249216{col 39}{space 1}   -2.82{col 48}{space 3}0.005{col 56}{space 4}-2.029066{col 69}{space 3}-.3634042
{txt}{space 7}gulfwar {c |}{col 16}{res}{space 2} 1.460522{col 28}{space 2} 1.352413{col 39}{space 1}    1.08{col 48}{space 3}0.280{col 56}{space 4}-1.190158{col 69}{space 3} 4.111202
{txt}{space 3}saddampre90 {c |}{col 16}{res}{space 2} 2.441351{col 28}{space 2} .6455812{col 39}{space 1}    3.78{col 48}{space 3}0.000{col 56}{space 4} 1.176035{col 69}{space 3} 3.706667
{txt}{space 2}saddampost90 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 7}iraqwar {c |}{col 16}{res}{space 2} .7217947{col 28}{space 2}   .42541{col 39}{space 1}    1.70{col 48}{space 3}0.090{col 56}{space 4}-.1119936{col 69}{space 3} 1.555583
{txt}{space 2}insurgency03 {c |}{col 16}{res}{space 2} .4893173{col 28}{space 2} .1775751{col 39}{space 1}    2.76{col 48}{space 3}0.006{col 56}{space 4} .1412766{col 69}{space 3} .8373581
{txt}{space 7}crime03 {c |}{col 16}{res}{space 2}-1.104428{col 28}{space 2} .3118467{col 39}{space 1}   -3.54{col 48}{space 3}0.000{col 56}{space 4}-1.715636{col 69}{space 3}-.4932193
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-1.055057{col 28}{space 2} .2597265{col 39}{space 1}   -4.06{col 48}{space 3}0.000{col 56}{space 4}-1.564111{col 69}{space 3} -.546002
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. vif, uncentered

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
insurgency03 {c |} {res}     1.01    0.992203
{txt}{space 5}iraqwar {c |} {res}     1.01    0.992203
{txt}{space 5}crime03 {c |} {res}     1.00    1.000000
{txt}{space 5}gulfwar {c |} {res}     1.00    1.000000
{txt}{space 4}iraniraq {c |} {res}     1.00    1.000000
{txt}{space 1}saddampre90 {c |} {res}     1.00    1.000000
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     1.00
{txt}
{com}. linktest

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-152.36517}  
Iteration 1:{space 2}Log likelihood = {res:-133.39928}  
Iteration 2:{space 2}Log likelihood = {res:-132.65839}  
Iteration 3:{space 2}Log likelihood = {res:-132.63939}  
Iteration 4:{space 2}Log likelihood = {res:-132.63938}  
Iteration 5:{space 2}Log likelihood = {res:-132.63938}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:LR chi2({res:2})}{col 70} = {res}{ralign 6:39.45}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-132.63938}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1295}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}_hat {c |}{col 16}{res}{space 2} 1.020416{col 28}{space 2} .2943838{col 39}{space 1}    3.47{col 48}{space 3}0.001{col 56}{space 4} .4434342{col 69}{space 3} 1.597397
{txt}{space 8}_hatsq {c |}{col 16}{res}{space 2} .0118511{col 28}{space 2} .1353971{col 39}{space 1}    0.09{col 48}{space 3}0.930{col 56}{space 4}-.2535224{col 69}{space 3} .2772246
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0003948{col 28}{space 2}  .241416{col 39}{space 1}   -0.00{col 48}{space 3}0.999{col 56}{space 4}-.4735614{col 69}{space 3} .4727718
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lfit, group(10)
{txt}{p 0 6 2}note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: {res}revdgsunnibias

{txt}{ralign 23:Number of observations} = {res}{ralign 6:278}
{txt}{ralign 23:Number of groups} = {res}{ralign 6:5}
{txt}{ralign 23:Hosmer–Lemeshow chi2({res:3})} = {res}{ralign 6:0.13}
{txt}{ralign 23:Prob > chi2} = {res}{ralign 6:0.9880}

{txt}{p 0 6 0}Warning: There are only 5 distinct quantiles because of ties.{p_end}

{com}. estat gof, group(10)
{txt}{p 0 6 2}note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: {res}revdgsunnibias

{txt}{ralign 23:Number of observations} = {res}{ralign 6:278}
{txt}{ralign 23:Number of groups} = {res}{ralign 6:5}
{txt}{ralign 23:Hosmer–Lemeshow chi2({res:3})} = {res}{ralign 6:0.13}
{txt}{ralign 23:Prob > chi2} = {res}{ralign 6:0.9880}

{txt}{p 0 6 0}Warning: There are only 5 distinct quantiles because of ties.{p_end}

{com}. 
. logit revdgsunnibias   heardkilloutgroup sawkilloutgroup punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib iraniraq-crime03 hadcovid diedcovid female age professional laborer unemployed westmosul if mosul==1, cluster(location)

{txt}note: {bf:saddampost90} omitted because of collinearity.
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-152.36517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-96.323279}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-89.959385}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-89.240484}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-89.231897}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-89.231886}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-89.231886}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(4)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-89.231886}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.4144}

{txt}{ralign 83:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}   revdgsunnibias{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
heardkilloutgroup {c |}{col 19}{res}{space 2}  .530738{col 31}{space 2} .2151305{col 42}{space 1}    2.47{col 51}{space 3}0.014{col 59}{space 4}   .10909{col 72}{space 3} .9523861
{txt}{space 2}sawkilloutgroup {c |}{col 19}{res}{space 2} 1.172257{col 31}{space 2} .9850893{col 42}{space 1}    1.19{col 51}{space 3}0.234{col 59}{space 4}-.7584822{col 72}{space 3} 3.102997
{txt}{space 5}punishedisis {c |}{col 19}{res}{space 2} .5547833{col 31}{space 2} 2.470934{col 42}{space 1}    0.22{col 51}{space 3}0.822{col 59}{space 4}-4.288158{col 72}{space 3} 5.397724
{txt}{space 7}injuredlib {c |}{col 19}{res}{space 2} 1.145895{col 31}{space 2} 2.295243{col 42}{space 1}    0.50{col 51}{space 3}0.618{col 59}{space 4}-3.352698{col 72}{space 3} 5.644487
{txt}{space 4}imprisonedlib {c |}{col 19}{res}{space 2}-.3939337{col 31}{space 2} 1.086055{col 42}{space 1}   -0.36{col 51}{space 3}0.717{col 59}{space 4}-2.522562{col 72}{space 3} 1.734695
{txt}{space 6}dsexassault {c |}{col 19}{res}{space 2}-.2799403{col 31}{space 2} .5886099{col 42}{space 1}   -0.48{col 51}{space 3}0.634{col 59}{space 4}-1.433595{col 72}{space 3}  .873714
{txt}{space 2}fampunishedisis {c |}{col 19}{res}{space 2} 4.366508{col 31}{space 2} 1.277803{col 42}{space 1}    3.42{col 51}{space 3}0.001{col 59}{space 4}  1.86206{col 72}{space 3} 6.870956
{txt}{space 3}faminjuredisis {c |}{col 19}{res}{space 2}-.5716518{col 31}{space 2} .6657554{col 42}{space 1}   -0.86{col 51}{space 3}0.391{col 59}{space 4}-1.876508{col 72}{space 3} .7332048
{txt}{space 4}faminjuredlib {c |}{col 19}{res}{space 2} 5.099172{col 31}{space 2} 1.760219{col 42}{space 1}    2.90{col 51}{space 3}0.004{col 59}{space 4} 1.649206{col 72}{space 3} 8.549138
{txt}{space 2}womenabusedisis {c |}{col 19}{res}{space 2} 2.155394{col 31}{space 2} 2.349808{col 42}{space 1}    0.92{col 51}{space 3}0.359{col 59}{space 4}-2.450144{col 72}{space 3} 6.760933
{txt}{space 5}fleehomeisis {c |}{col 19}{res}{space 2} 2.824626{col 31}{space 2} 2.256909{col 42}{space 1}    1.25{col 51}{space 3}0.211{col 59}{space 4}-1.598834{col 72}{space 3} 7.248087
{txt}{space 2}homedamagedisis {c |}{col 19}{res}{space 2} 1.928352{col 31}{space 2} 1.830421{col 42}{space 1}    1.05{col 51}{space 3}0.292{col 59}{space 4}-1.659208{col 72}{space 3} 5.515913
{txt}{space 6}fledhomelib {c |}{col 19}{res}{space 2} 2.867138{col 31}{space 2} .7774281{col 42}{space 1}    3.69{col 51}{space 3}0.000{col 59}{space 4} 1.343407{col 72}{space 3} 4.390869
{txt}{space 3}homedamagedlib {c |}{col 19}{res}{space 2} .6419079{col 31}{space 2} 1.041376{col 42}{space 1}    0.62{col 51}{space 3}0.538{col 59}{space 4}-1.399152{col 72}{space 3} 2.682968
{txt}{space 4}homelootedlib {c |}{col 19}{res}{space 2} 1.036548{col 31}{space 2} .7430064{col 42}{space 1}    1.40{col 51}{space 3}0.163{col 59}{space 4}-.4197173{col 72}{space 3} 2.492814
{txt}{space 9}iraniraq {c |}{col 19}{res}{space 2}-1.908163{col 31}{space 2} .2996129{col 42}{space 1}   -6.37{col 51}{space 3}0.000{col 59}{space 4}-2.495394{col 72}{space 3}-1.320933
{txt}{space 10}gulfwar {c |}{col 19}{res}{space 2} 3.419173{col 31}{space 2} .8032988{col 42}{space 1}    4.26{col 51}{space 3}0.000{col 59}{space 4} 1.844737{col 72}{space 3}  4.99361
{txt}{space 6}saddampre90 {c |}{col 19}{res}{space 2} 3.092291{col 31}{space 2} .2996459{col 42}{space 1}   10.32{col 51}{space 3}0.000{col 59}{space 4} 2.504996{col 72}{space 3} 3.679586
{txt}{space 5}saddampost90 {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 10}iraqwar {c |}{col 19}{res}{space 2} .6376442{col 31}{space 2} .3846717{col 42}{space 1}    1.66{col 51}{space 3}0.097{col 59}{space 4}-.1162986{col 72}{space 3} 1.391587
{txt}{space 5}insurgency03 {c |}{col 19}{res}{space 2} 1.835063{col 31}{space 2} .2331047{col 42}{space 1}    7.87{col 51}{space 3}0.000{col 59}{space 4} 1.378187{col 72}{space 3}  2.29194
{txt}{space 10}crime03 {c |}{col 19}{res}{space 2}-1.088412{col 31}{space 2} .5222538{col 42}{space 1}   -2.08{col 51}{space 3}0.037{col 59}{space 4}-2.112011{col 72}{space 3}-.0648133
{txt}{space 9}hadcovid {c |}{col 19}{res}{space 2} .3223109{col 31}{space 2}  .624302{col 42}{space 1}    0.52{col 51}{space 3}0.606{col 59}{space 4}-.9012985{col 72}{space 3}  1.54592
{txt}{space 8}diedcovid {c |}{col 19}{res}{space 2} -1.24637{col 31}{space 2} .6430648{col 42}{space 1}   -1.94{col 51}{space 3}0.053{col 59}{space 4}-2.506754{col 72}{space 3} .0140136
{txt}{space 11}female {c |}{col 19}{res}{space 2} 2.297415{col 31}{space 2}  .446451{col 42}{space 1}    5.15{col 51}{space 3}0.000{col 59}{space 4} 1.422387{col 72}{space 3} 3.172442
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0084638{col 31}{space 2} .0116942{col 42}{space 1}    0.72{col 51}{space 3}0.469{col 59}{space 4}-.0144565{col 72}{space 3}  .031384
{txt}{space 5}professional {c |}{col 19}{res}{space 2}-.4070385{col 31}{space 2} .5075865{col 42}{space 1}   -0.80{col 51}{space 3}0.423{col 59}{space 4} -1.40189{col 72}{space 3} .5878128
{txt}{space 10}laborer {c |}{col 19}{res}{space 2}-1.287524{col 31}{space 2} .1531206{col 42}{space 1}   -8.41{col 51}{space 3}0.000{col 59}{space 4}-1.587635{col 72}{space 3}-.9874135
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.3289734{col 31}{space 2}    .9157{col 42}{space 1}   -0.36{col 51}{space 3}0.719{col 59}{space 4}-2.123712{col 72}{space 3} 1.465766
{txt}{space 8}westmosul {c |}{col 19}{res}{space 2} -.219031{col 31}{space 2} .3537583{col 42}{space 1}   -0.62{col 51}{space 3}0.536{col 59}{space 4}-.9123844{col 72}{space 3} .4743224
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-4.410514{col 31}{space 2} 2.183299{col 42}{space 1}   -2.02{col 51}{space 3}0.043{col 59}{space 4}-8.689701{col 72}{space 3}-.1313277
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. vif, uncentered

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
{space 9}age {c |} {res}    10.16    0.098403
{txt}womenabuse~s {c |} {res}     6.92    0.144522
{txt}homedamage~s {c |} {res}     4.45    0.224926
{txt}punishedisis {c |} {res}     4.42    0.225996
{txt}professional {c |} {res}     4.16    0.240348
{txt}{space 4}hadcovid {c |} {res}     3.59    0.278869
{txt}homelooted~b {c |} {res}     3.31    0.301738
{txt}faminjured~b {c |} {res}     3.29    0.303605
{txt}faminjured~s {c |} {res}     3.21    0.311569
{txt}{space 5}crime03 {c |} {res}     2.95    0.339010
{txt}{space 3}diedcovid {c |} {res}     2.60    0.385251
{txt}imprisoned~b {c |} {res}     2.59    0.385729
{txt}insurgency03 {c |} {res}     2.44    0.409222
{txt}{space 4}iraniraq {c |} {res}     2.33    0.428437
{txt}fleehomeisis {c |} {res}     2.23    0.447435
{txt}heardkillo~p {c |} {res}     2.10    0.475899
{txt}fampunishe~s {c |} {res}     1.94    0.516356
{txt}{space 5}laborer {c |} {res}     1.91    0.523509
{txt}{space 3}westmosul {c |} {res}     1.87    0.534733
{txt}{space 2}unemployed {c |} {res}     1.77    0.565780
{txt}sawkillout~p {c |} {res}     1.65    0.606118
{txt}homedamage~b {c |} {res}     1.64    0.610741
{txt}{space 6}female {c |} {res}     1.56    0.642155
{txt}{space 1}fledhomelib {c |} {res}     1.55    0.645723
{txt}{space 5}iraqwar {c |} {res}     1.55    0.646712
{txt}{space 2}injuredlib {c |} {res}     1.43    0.698642
{txt}{space 1}saddampre90 {c |} {res}     1.37    0.729059
{txt}{space 5}gulfwar {c |} {res}     1.25    0.797902
{txt}{space 1}dsexassault {c |} {res}     1.23    0.812789
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     2.81
{txt}
{com}. linktest

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-152.36517}  
Iteration 1:{space 2}Log likelihood = {res:-93.053657}  
Iteration 2:{space 2}Log likelihood = {res:-88.016447}  
Iteration 3:{space 2}Log likelihood = {res: -87.95868}  
Iteration 4:{space 2}Log likelihood = {res: -87.95858}  
Iteration 5:{space 2}Log likelihood = {res: -87.95858}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:LR chi2({res:2})}{col 70} = {res}{ralign 6:128.81}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 9:-87.95858}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.4227}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}revdgsunnibias{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}_hat {c |}{col 16}{res}{space 2} 1.216398{col 28}{space 2} .2090165{col 39}{space 1}    5.82{col 48}{space 3}0.000{col 56}{space 4} .8067335{col 69}{space 3} 1.626063
{txt}{space 8}_hatsq {c |}{col 16}{res}{space 2} .0917036{col 28}{space 2} .0543028{col 39}{space 1}    1.69{col 48}{space 3}0.091{col 56}{space 4} -.014728{col 69}{space 3} .1981351
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0715975{col 28}{space 2} .2355897{col 39}{space 1}   -0.30{col 48}{space 3}0.761{col 56}{space 4}-.5333448{col 69}{space 3} .3901498
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lfit, group(10)
{txt}{p 0 6 2}note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: {res}revdgsunnibias

{txt}{ralign 23:Number of observations} = {res}{ralign 6:278}
{txt}{ralign 23:Number of groups} = {res}{ralign 6:10}
{txt}{ralign 23:Hosmer–Lemeshow chi2({res:8})} = {res}{ralign 6:12.57}
{txt}{ralign 23:Prob > chi2} = {res}{ralign 6:0.1277}
{txt}
{com}. estat gof, group(10)
{txt}{p 0 6 2}note: obs collapsed on 10 quantiles of estimated probabilities.

Goodness-of-fit test after logistic model
Variable: {res}revdgsunnibias

{txt}{ralign 23:Number of observations} = {res}{ralign 6:278}
{txt}{ralign 23:Number of groups} = {res}{ralign 6:10}
{txt}{ralign 23:Hosmer–Lemeshow chi2({res:8})} = {res}{ralign 6:12.57}
{txt}{ralign 23:Prob > chi2} = {res}{ralign 6:0.1277}
{txt}
{com}. 
. *Sensitivity Analysis – Victimization and Altruism
. 
. regsensitivity bounds revdgsunnibias mmx_tetraisisvictimall  female age education income professional laborer unemployed westmosul, dmp
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=         278
{col 48}{txt}Beta(short){col 67}{res}=       0.636
{txt}Treatment{col 18}{res}: mmx_tetraisisvictimall{col 48}{txt}Beta(medium){col 67}{res}=       0.570
{txt}Outcome{col 18}{res}: revdgsunnibias{col 48}{txt}R2(short){col 67}{res}=       0.056
{col 48}{txt}R2(medium){col 67}{res}=       0.095
{col 48}{txt}Var(Y){col 67}{res}=       0.182
{col 48}{txt}Var(X){col 67}{res}=       0.025
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.024

{txt}Hypothesis{col 18}{res}: Beta > 0         {col 48}{txt}Breakdown point{col 67}{res}=        71.5%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res} 0.5698{txt}, {res} 0.5698{txt} ]
{col 2}{res}0.099{col 35}{txt}[{res} 0.5158{txt}, {res} 0.6237{txt} ]
{col 2}{res}0.197{col 35}{txt}[{res} 0.4602{txt}, {res} 0.6794{txt} ]
{col 2}{res}0.296{col 35}{txt}[{res} 0.4009{txt}, {res} 0.7387{txt} ]
{col 2}{res}0.394{col 35}{txt}[{res} 0.3353{txt}, {res} 0.8043{txt} ]
{col 2}{res}0.493{col 35}{txt}[{res} 0.2593{txt}, {res} 0.8803{txt} ]
{col 2}{res}0.591{col 35}{txt}[{res} 0.1658{txt}, {res} 0.9737{txt} ]
{col 2}{res}0.690{col 35}{txt}[{res} 0.0405{txt}, {res} 1.0991{txt} ]
{col 2}{res}0.788{col 35}{txt}[{res}-0.1541{txt}, {res} 1.2936{txt} ]
{col 2}{res}0.887{col 35}{txt}[{res}-0.5699{txt}, {res} 1.7094{txt} ]
{col 2}{res}0.979{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. *Power Calculations – Awareness and Victimization Effects
. 
. *In addition to G*Power you can use powerlog  command in STATA. 
. 
. *Power Calculations for Awareness of Out-Group Suffering
. 
. sum dsaworheardkilloutgroup

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
dsaworhear~p {c |}{res}        278    .0863309    .2813586          0          1
{txt}
{com}. logistic revdgsunnibias dsaworheardkilloutgroup , cluster(location)
{res}
{txt}{col 1}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:278}
{txt}{col 56}{lalign 13:Wald chi2({res:1})}{col 69} = {res}{ralign 7:1189.55}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-145.04879}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0480}

{txt}{ralign 89:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         revdgsunnibias{col 25}{c |} Odds ratio{col 37}   std. err.{col 49}      z{col 57}   P>|z|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dsaworheardkilloutgroup {c |}{col 25}{res}{space 2} 5.438462{col 37}{space 2} .2670355{col 48}{space 1}   34.49{col 57}{space 3}0.000{col 65}{space 4} 4.939477{col 78}{space 3} 5.987853
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} .2574257{col 37}{space 2} .0398585{col 48}{space 1}   -8.76{col 57}{space 3}0.000{col 65}{space 4} .1900449{col 78}{space 3} .3486967
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds{txt}.{p_end}

{com}. margins, at( dsaworheardkilloutgroup =(0.09 0.37))
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:278}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(revdgsunnibias), predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:dsaworheardkil~p} = {res:{ralign 3:.09}}
{lalign 7:2._at: }{space 0}{lalign 16:dsaworheardkil~p} = {res:{ralign 3:.37}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2306563{col 26}{space 2} .0269294{col 37}{space 1}    8.57{col 46}{space 3}0.000{col 54}{space 4} .1778756{col 67}{space 3}  .283437
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3251003{col 26}{space 2} .0312925{col 37}{space 1}   10.39{col 46}{space 3}0.000{col 54}{space 4} .2637681{col 67}{space 3} .3864324
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. powerlog , p1(.23) p2(0.33)

{txt} Logistic regression power analysis
 One-tailed test: alpha=.05  p1=.23  p2=.33  rsq=0  odds ratio=1.648929266709929

 power          n
{res} 0.60         101
 0.65         115
 0.70         130
 0.75         148
 0.80         169
 0.85         196
 0.90         232
{txt}
{com}. 
. *Power Calculations for ISIS Victimization on Out-Group Altruism
. 
. sum mmx_tetraisisvictimall

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
mmx_tetrai~l {c |}{res}        278    .3470284    .1579657          0          1
{txt}
{com}. logistic revdgsunnibias mmx_tetraisisvictimall , cluster(location)
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:94.04}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-144.88814}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0491}

{txt}{ralign 88:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}        revdgsunnibias{col 24}{c |} Odds ratio{col 36}   std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_tetraisisvictimall {c |}{col 24}{res}{space 2} 30.78111{col 36}{space 2} 10.87761{col 47}{space 1}    9.70{col 56}{space 3}0.000{col 64}{space 4} 15.39862{col 77}{space 3} 61.52998
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} .0885536{col 36}{space 2} .0094445{col 47}{space 1}  -22.73{col 56}{space 3}0.000{col 64}{space 4} .0718494{col 77}{space 3} .1091413
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds{txt}.{p_end}

{com}. margins, at(mmx_tetraisisvictimall=(0.35 0.51))
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:278}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(revdgsunnibias), predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:mmx_tetraisisv~l} = {res:{ralign 3:.35}}
{lalign 7:2._at: }{space 0}{lalign 16:mmx_tetraisisv~l} = {res:{ralign 3:.51}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2271048{col 26}{space 2} .0279928{col 37}{space 1}    8.11{col 46}{space 3}0.000{col 54}{space 4} .1722399{col 67}{space 3} .2819697
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .337059{col 26}{space 2}  .045821{col 37}{space 1}    7.36{col 46}{space 3}0.000{col 54}{space 4} .2472516{col 67}{space 3} .4268664
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. powerlog , p1(.23) p2(0.34)

{txt} Logistic regression power analysis
 One-tailed test: alpha=.05  p1=.23  p2=.34  rsq=0  odds ratio=1.724637681159421

 power          n
{res} 0.60          87
 0.65          99
 0.70         112
 0.75         127
 0.80         145
 0.85         168
 0.90         198
{txt}
{com}. 
. *Power Calculations for Pre-ISIS Victimization on Out-Group Altruism
. 
. sum mmx_tetrapreisisvictimall

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
mmx_tetrap~l {c |}{res}        278    .3020271    .3851143          0          1
{txt}
{com}. logistic revdgsunnibias mmx_tetrapreisisvictimall , cluster(location)
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:278}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:67.41}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-147.84261}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0297}

{txt}{ralign 91:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}           revdgsunnibias{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mmx_tetrapreisisvictimall {c |}{col 27}{res}{space 2} 2.875824{col 39}{space 2} .3699968{col 50}{space 1}    8.21{col 59}{space 3}0.000{col 67}{space 4} 2.234851{col 80}{space 3} 3.700632
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .2170674{col 39}{space 2}  .048398{col 50}{space 1}   -6.85{col 59}{space 3}0.000{col 67}{space 4} .1402194{col 80}{space 3} .3360323
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds{txt}.{p_end}

{com}. margins, at( mmx_tetrapreisisvictimall  =(0.30 0.68))
{res}
{txt}{col 1}Adjusted predictions{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:278}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(revdgsunnibias), predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:mmx_tetrapreis~l} = {res:{ralign 3:.3}}
{lalign 7:2._at: }{space 0}{lalign 16:mmx_tetrapreis~l} = {res:{ralign 3:.68}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2295868{col 26}{space 2} .0345879{col 37}{space 1}    6.64{col 46}{space 3}0.000{col 54}{space 4} .1617958{col 67}{space 3} .2973778
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3080531{col 26}{space 2} .0356572{col 37}{space 1}    8.64{col 46}{space 3}0.000{col 54}{space 4} .2381664{col 67}{space 3} .3779399
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. powerlog , p1(.23) p2(0.31)

{txt} Logistic regression power analysis
 One-tailed test: alpha=.05  p1=.23  p2=.31  rsq=0  odds ratio=1.504095778197858

 power          n
{res} 0.60         146
 0.65         166
 0.70         189
 0.75         215
 0.80         247
 0.85         286
 0.90         339
{txt}
{com}. 
. *Manuscript Table 3 Robustness Checks
. 
. reg closeoutgroup victimorder, robust 

{txt}Linear regression                               Number of obs     = {res}       278
                                                {txt}F(1, 276)         =  {res}    26.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1083
                                                {txt}Root MSE          =    {res} 1.4958

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}closeoutgr~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} 1.138784{col 26}{space 2} .2204513{col 37}{space 1}    5.17{col 46}{space 3}0.000{col 54}{space 4} .7048041{col 67}{space 3} 1.572763
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.418699{col 26}{space 2} .1981983{col 37}{space 1}   12.20{col 46}{space 3}0.000{col 54}{space 4} 2.028527{col 67}{space 3} 2.808872
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg closeoutgroup revdgsunnibias, robust

{txt}Linear regression                               Number of obs     = {res}       278
                                                {txt}F(1, 276)         =  {res}    31.73
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0864
                                                {txt}Root MSE          =    {res} 1.5141

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1} closeoutgroup{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} 1.090294{col 28}{space 2} .1935583{col 39}{space 1}    5.63{col 48}{space 3}0.000{col 56}{space 4} .7092564{col 69}{space 3} 1.471333
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.962736{col 28}{space 2} .1080759{col 39}{space 1}   27.41{col 48}{space 3}0.000{col 56}{space 4} 2.749978{col 69}{space 3} 3.175494
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg closeoutgroup victimorder revdgsunnibias dsaworheardkilloutgroup mmx_tetraisisvictimall mmx_tetrapreisisvictim hadcovid diedcovid female age professional laborer unemployed westmosul, cluster(location)

{txt}Linear regression                               Number of obs     = {res}       278
                                                {txt}{help j_robustsingular:F(4, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.2686
                                                {txt}Root MSE          =    {res} 1.3851

{txt}{ralign 91:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}            closeoutgroup{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}victimorder {c |}{col 27}{res}{space 2} .8564189{col 39}{space 2}   .17558{col 50}{space 1}    4.88{col 59}{space 3}0.005{col 67}{space 4} .4050761{col 80}{space 3} 1.307762
{txt}{space 11}revdgsunnibias {c |}{col 27}{res}{space 2} .9296706{col 39}{space 2} .1393369{col 50}{space 1}    6.67{col 59}{space 3}0.001{col 67}{space 4} .5714938{col 80}{space 3} 1.287848
{txt}{space 2}dsaworheardkilloutgroup {c |}{col 27}{res}{space 2}-.2389657{col 39}{space 2} .4253422{col 50}{space 1}   -0.56{col 59}{space 3}0.598{col 67}{space 4}-1.332343{col 80}{space 3} .8544113
{txt}{space 3}mmx_tetraisisvictimall {c |}{col 27}{res}{space 2}-.1257211{col 39}{space 2} .6179439{col 50}{space 1}   -0.20{col 59}{space 3}0.847{col 67}{space 4}-1.714197{col 80}{space 3} 1.462754
{txt}mmx_tetrapreisisvictimall {c |}{col 27}{res}{space 2} .6184523{col 39}{space 2} .0687862{col 50}{space 1}    8.99{col 59}{space 3}0.000{col 67}{space 4} .4416318{col 80}{space 3} .7952727
{txt}{space 17}hadcovid {c |}{col 27}{res}{space 2}-.1994803{col 39}{space 2} .2333306{col 50}{space 1}   -0.85{col 59}{space 3}0.432{col 67}{space 4}-.7992757{col 80}{space 3} .4003152
{txt}{space 16}diedcovid {c |}{col 27}{res}{space 2} .6993531{col 39}{space 2} .1129973{col 50}{space 1}    6.19{col 59}{space 3}0.002{col 67}{space 4} .4088842{col 80}{space 3}  .989822
{txt}{space 19}female {c |}{col 27}{res}{space 2} .2357776{col 39}{space 2} .3884444{col 50}{space 1}    0.61{col 59}{space 3}0.570{col 67}{space 4}-.7627504{col 80}{space 3} 1.234306
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0201402{col 39}{space 2} .0069324{col 50}{space 1}   -2.91{col 59}{space 3}0.034{col 67}{space 4}-.0379606{col 80}{space 3}-.0023199
{txt}{space 13}professional {c |}{col 27}{res}{space 2} .0627713{col 39}{space 2} .0644311{col 50}{space 1}    0.97{col 59}{space 3}0.375{col 67}{space 4}-.1028542{col 80}{space 3} .2283968
{txt}{space 18}laborer {c |}{col 27}{res}{space 2}-.1763774{col 39}{space 2} .0464572{col 50}{space 1}   -3.80{col 59}{space 3}0.013{col 67}{space 4}-.2957994{col 80}{space 3}-.0569554
{txt}{space 15}unemployed {c |}{col 27}{res}{space 2}-.3369912{col 39}{space 2} .1917773{col 50}{space 1}   -1.76{col 59}{space 3}0.139{col 67}{space 4}-.8299705{col 80}{space 3} .1559881
{txt}{space 16}westmosul {c |}{col 27}{res}{space 2} .0167537{col 39}{space 2} .0519787{col 50}{space 1}    0.32{col 59}{space 3}0.760{col 67}{space 4}-.1168618{col 80}{space 3} .1503691
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 2.813502{col 39}{space 2} .5571198{col 50}{space 1}    5.05{col 59}{space 3}0.004{col 67}{space 4}  1.38138{col 80}{space 3} 4.245624
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Construction of Outgroup Empathy Index
. 
. factor closeshia-closegay if mosul==1
{txt}(obs=273)

Factor analysis/correlation{col 50}Number of obs    = {res}       273
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       2
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      11

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.76283      1.04442            0.9058       0.9058
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.71840      0.71884            0.3691       1.2750
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.00044      0.08469           -0.0002       1.2747
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.08513      0.11240           -0.0437       1.2310
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.19753      0.05446           -0.1015       1.1295
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.25199            .           -0.1295       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}15{txt}) ={res}  373.32{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:closeshia}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7387}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1221}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4394}}}{space 1}
{space 4}{space 0}{ralign 12:closeyazidi}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7648}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0968}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4058}}}{space 1}
{space 4}{space 0}{ralign 12:closekurd}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5483}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2394}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6420}}}{space 1}
{space 4}{space 0}{ralign 12:closechris~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4042}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0622}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8327}}}{space 1}
{space 4}{space 0}{ralign 12:closeforei~r}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2434}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5827}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6012}}}{space 1}
{space 4}{space 0}{ralign 12:closegay}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3301}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5417}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5976}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. *Sensitivity Analysis – Empathy, Victimization Priming, and Out-Group Altruism
. 
. regsensitivity bounds closeoutgroup victimorder female age education income professional laborer unemployed westmosul, dmp
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=         278
{col 48}{txt}Beta(short){col 67}{res}=       1.139
{txt}Treatment{col 18}{res}: victimorder{col 48}{txt}Beta(medium){col 67}{res}=       1.140
{txt}Outcome{col 18}{res}: closeoutgroup{col 48}{txt}R2(short){col 67}{res}=       0.108
{col 48}{txt}R2(medium){col 67}{res}=       0.163
{col 48}{txt}Var(Y){col 67}{res}=       2.500
{col 48}{txt}Var(X){col 67}{res}=       0.209
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.185

{txt}Hypothesis{col 18}{res}: Beta > 0         {col 48}{txt}Breakdown point{col 67}{res}=        66.8%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res} 1.1400{txt}, {res} 1.1400{txt} ]
{col 2}{res}0.094{col 35}{txt}[{res} 1.0260{txt}, {res} 1.2539{txt} ]
{col 2}{res}0.188{col 35}{txt}[{res} 0.9085{txt}, {res} 1.3714{txt} ]
{col 2}{res}0.283{col 35}{txt}[{res} 0.7833{txt}, {res} 1.4966{txt} ]
{col 2}{res}0.377{col 35}{txt}[{res} 0.6450{txt}, {res} 1.6349{txt} ]
{col 2}{res}0.471{col 35}{txt}[{res} 0.4852{txt}, {res} 1.7947{txt} ]
{col 2}{res}0.565{col 35}{txt}[{res} 0.2894{txt}, {res} 1.9906{txt} ]
{col 2}{res}0.659{col 35}{txt}[{res} 0.0281{txt}, {res} 2.2518{txt} ]
{col 2}{res}0.753{col 35}{txt}[{res}-0.3728{txt}, {res} 2.6528{txt} ]
{col 2}{res}0.848{col 35}{txt}[{res}-1.2047{txt}, {res} 3.4846{txt} ]
{col 2}{res}0.942{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. 
. *Balance Tests on Victimization Order Randomization
. 
. ksmirnov sawkillsunni if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.7073       0.000
{txt}Combined K-S       {res}  0.7073       0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov dheardkilloutgroup if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0085       0.992
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0085       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov dsawkilloutgroup if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0732       0.538
{txt}Combined K-S       {res}  0.0732       0.916

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov punishedisis if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.2256       0.003
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.2256       0.006

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov dsexassault if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.1605       0.051
{txt}Combined K-S       {res}  0.1605       0.102

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov fampunishedisis if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0612       0.648
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0612       0.982

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov faminjuredisis if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0969       0.337
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0969       0.649

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov womenabusedisis if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.3830       0.000
{txt}Combined K-S       {res}  0.3830       0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov fleehomeisis if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0686       0.581
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0686       0.949

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov homedamagedisis if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.1345       0.123
{txt}Combined K-S       {res}  0.1345       0.246

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov injuredlib if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0062       0.996
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0062       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov faminjuredlib if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0918       0.377
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0918       0.714

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov famkilledlib if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0142       0.977
{txt}Combined K-S       {res}  0.0142       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov homedamagedlib if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0022       0.999
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0022       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov imprisonedlib if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1990       0.010
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.1990       0.021

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov fledhomelib if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0777       0.498
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0777       0.877

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov homelootedlib if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0948       0.354
{txt}Combined K-S       {res}  0.0948       0.676

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov womenabusedlib if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.4176       0.000
{txt}Combined K-S       {res}  0.4176       0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov iraniraq if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0759       0.514
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0759       0.893

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov gulfwar if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0255       0.928
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0255       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov saddampre90 if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0182       0.963
{txt}Combined K-S       {res}  0.0182       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov iraqwar if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0233       0.939
{txt}Combined K-S       {res}  0.0233       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov insurgency03 if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0833       0.449
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0833       0.818

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov crime03 if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.1096       0.249
{txt}Combined K-S       {res}  0.1096       0.491

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov hadcovid if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.4812       0.000
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.4812       0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov diedcovid if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.3747       0.000
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.3747       0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov female if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0586       0.672
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0586       0.989

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov age if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1420       0.097
{txt}1                  {res} -0.0473       0.772
{txt}Combined K-S       {res}  0.1420       0.194

{txt}Note: Ties exist in combined dataset;
      there are 42 unique values out of 278 observations.

{com}. ksmirnov professional if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1273       0.154
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.1273       0.306

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov laborer if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0635       0.628
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0635       0.974

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov unemployed if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0096       0.989
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0096       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. ksmirnov westmosul if mosul==1, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.2143       0.005
{txt}Combined K-S       {res}  0.2143       0.010

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 278 observations.

{com}. 
. *See also t-tests
. 
. iebaltab sawkillsunni dheardkilloutgroup punishedisis dsexassault fampunishedisis faminjuredisis womenabusedisis fleehomeisis homedamagedisis injuredlib faminjuredlib famkilledlib homedamagedlib imprisonedlib fledhomelib homelootedlib womenabusedlib iraniraq gulfwar saddampre90 iraqwar insurgency03 crime03 hadcovid diedcovid female age professional laborer unemployed westmosul, groupvar(victimorder) vce(cluster location) savexlsx(balancevictimorder) control(0)

{res}{phang}Balance table saved in Excel format to: {browse "balancevictimorder.xlsx":balancevictimorder.xlsx}{p_end}
{txt}
{com}. 
. *Correlates of Empathy (OLS Regression)
. 
. reg closeoutgroup victimorder if mosul==1, robust

{txt}Linear regression                               Number of obs     = {res}       278
                                                {txt}F(1, 276)         =  {res}    26.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1083
                                                {txt}Root MSE          =    {res} 1.4958

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}closeoutgr~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} 1.138784{col 26}{space 2} .2204513{col 37}{space 1}    5.17{col 46}{space 3}0.000{col 54}{space 4} .7048041{col 67}{space 3} 1.572763
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.418699{col 26}{space 2} .1981983{col 37}{space 1}   12.20{col 46}{space 3}0.000{col 54}{space 4} 2.028527{col 67}{space 3} 2.808872
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg closeoutgroup victimorder heardkilloutgroup sawkilloutgroup sawkillsunni punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib, cluster(location)

{txt}Linear regression                               Number of obs     = {res}       278
                                                {txt}{help j_robustsingular:F(2, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.2439
                                                {txt}Root MSE          =    {res} 1.4191

{txt}{ralign 83:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    closeoutgroup{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}victimorder {c |}{col 19}{res}{space 2} .7889584{col 31}{space 2} .1591062{col 42}{space 1}    4.96{col 51}{space 3}0.004{col 59}{space 4} .3799629{col 72}{space 3} 1.197954
{txt}heardkilloutgroup {c |}{col 19}{res}{space 2} -.092613{col 31}{space 2} .4135771{col 42}{space 1}   -0.22{col 51}{space 3}0.832{col 59}{space 4}-1.155747{col 72}{space 3} .9705209
{txt}{space 2}sawkilloutgroup {c |}{col 19}{res}{space 2} 1.549859{col 31}{space 2} .7735532{col 42}{space 1}    2.00{col 51}{space 3}0.101{col 59}{space 4}-.4386227{col 72}{space 3} 3.538341
{txt}{space 5}sawkillsunni {c |}{col 19}{res}{space 2}-.3511542{col 31}{space 2} .2300142{col 42}{space 1}   -1.53{col 51}{space 3}0.187{col 59}{space 4}-.9424245{col 72}{space 3} .2401161
{txt}{space 5}punishedisis {c |}{col 19}{res}{space 2} .7595511{col 31}{space 2} .1332866{col 42}{space 1}    5.70{col 51}{space 3}0.002{col 59}{space 4} .4169269{col 72}{space 3} 1.102175
{txt}{space 7}injuredlib {c |}{col 19}{res}{space 2}-.1893951{col 31}{space 2} .5572102{col 42}{space 1}   -0.34{col 51}{space 3}0.748{col 59}{space 4} -1.62175{col 72}{space 3} 1.242959
{txt}{space 4}imprisonedlib {c |}{col 19}{res}{space 2}-.0468428{col 31}{space 2} .3376292{col 42}{space 1}   -0.14{col 51}{space 3}0.895{col 59}{space 4}-.9147462{col 72}{space 3} .8210607
{txt}{space 6}dsexassault {c |}{col 19}{res}{space 2} .4805511{col 31}{space 2} .4909351{col 42}{space 1}    0.98{col 51}{space 3}0.373{col 59}{space 4}-.7814377{col 72}{space 3}  1.74254
{txt}{space 2}fampunishedisis {c |}{col 19}{res}{space 2} .1480836{col 31}{space 2} .0922837{col 42}{space 1}    1.60{col 51}{space 3}0.169{col 59}{space 4}-.0891392{col 72}{space 3} .3853064
{txt}{space 3}faminjuredisis {c |}{col 19}{res}{space 2} 1.194795{col 31}{space 2} .2055203{col 42}{space 1}    5.81{col 51}{space 3}0.002{col 59}{space 4} .6664883{col 72}{space 3} 1.723102
{txt}{space 4}faminjuredlib {c |}{col 19}{res}{space 2}-.5262358{col 31}{space 2} .4201358{col 42}{space 1}   -1.25{col 51}{space 3}0.266{col 59}{space 4}-1.606229{col 72}{space 3} .5537576
{txt}{space 2}womenabusedisis {c |}{col 19}{res}{space 2}-.1095169{col 31}{space 2} .2650058{col 42}{space 1}   -0.41{col 51}{space 3}0.697{col 59}{space 4} -.790736{col 72}{space 3} .5717022
{txt}{space 5}fleehomeisis {c |}{col 19}{res}{space 2}  .177011{col 31}{space 2} .2058935{col 42}{space 1}    0.86{col 51}{space 3}0.429{col 59}{space 4} -.352255{col 72}{space 3}  .706277
{txt}{space 2}homedamagedisis {c |}{col 19}{res}{space 2}-.5895311{col 31}{space 2} .0971953{col 42}{space 1}   -6.07{col 51}{space 3}0.002{col 59}{space 4}-.8393797{col 72}{space 3}-.3396826
{txt}{space 6}fledhomelib {c |}{col 19}{res}{space 2}   .28565{col 31}{space 2} .4629712{col 42}{space 1}    0.62{col 51}{space 3}0.564{col 59}{space 4}-.9044554{col 72}{space 3} 1.475755
{txt}{space 3}homedamagedlib {c |}{col 19}{res}{space 2} 1.194708{col 31}{space 2} .9489381{col 42}{space 1}    1.26{col 51}{space 3}0.264{col 59}{space 4}-1.244615{col 72}{space 3} 3.634032
{txt}{space 4}homelootedlib {c |}{col 19}{res}{space 2} 1.085082{col 31}{space 2} .3737846{col 42}{space 1}    2.90{col 51}{space 3}0.034{col 59}{space 4} .1242378{col 72}{space 3} 2.045926
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.344164{col 31}{space 2} .2431497{col 42}{space 1}    9.64{col 51}{space 3}0.000{col 59}{space 4} 1.719128{col 72}{space 3}   2.9692
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg closeoutgroup victimorder  iraniraq-crime03 , cluster(location)
{txt}{p 0 6 2}note: {bf:saddampost90} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       278
                                                {txt}{help j_robustsingular:F(2, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.1919
                                                {txt}Root MSE          =    {res} 1.4397

{txt}{ralign 78:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}closeoutgr~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} 1.128049{col 26}{space 2} .0746488{col 37}{space 1}   15.11{col 46}{space 3}0.000{col 54}{space 4} .9361581{col 67}{space 3}  1.31994
{txt}{space 4}iraniraq {c |}{col 14}{res}{space 2}-.8704257{col 26}{space 2} .1340783{col 37}{space 1}   -6.49{col 46}{space 3}0.001{col 54}{space 4}-1.215085{col 67}{space 3}-.5257665
{txt}{space 5}gulfwar {c |}{col 14}{res}{space 2} .4345492{col 26}{space 2} .0798553{col 37}{space 1}    5.44{col 46}{space 3}0.003{col 54}{space 4} .2292745{col 67}{space 3} .6398239
{txt}{space 1}saddampre90 {c |}{col 14}{res}{space 2} .5857686{col 26}{space 2} .2349337{col 37}{space 1}    2.49{col 46}{space 3}0.055{col 54}{space 4}-.0181476{col 67}{space 3} 1.189685
{txt}saddampost90 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 5}iraqwar {c |}{col 14}{res}{space 2} .2651742{col 26}{space 2} .8940849{col 37}{space 1}    0.30{col 46}{space 3}0.779{col 54}{space 4}-2.033144{col 67}{space 3} 2.563493
{txt}insurgency03 {c |}{col 14}{res}{space 2} .4182843{col 26}{space 2} .1226687{col 37}{space 1}    3.41{col 46}{space 3}0.019{col 54}{space 4} .1029545{col 67}{space 3} .7336141
{txt}{space 5}crime03 {c |}{col 14}{res}{space 2}-.4352107{col 26}{space 2} .1152253{col 37}{space 1}   -3.78{col 46}{space 3}0.013{col 54}{space 4}-.7314067{col 67}{space 3}-.1390146
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.570735{col 26}{space 2} .0857397{col 37}{space 1}   29.98{col 46}{space 3}0.000{col 54}{space 4} 2.350335{col 67}{space 3} 2.791136
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg closeoutgroup victimorder heardkilloutgroup sawkilloutgroup sawkillsunni punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib iraniraq-crime03 hadcovid diedcovid female age professional laborer unemployed westmosul, cluster(location)
{txt}{p 0 6 2}note: {bf:saddampost90} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       278
                                                {txt}{help j_robustsingular:F(4, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.3558
                                                {txt}Root MSE          =    {res} 1.3467

{txt}{ralign 83:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    closeoutgroup{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}victimorder {c |}{col 19}{res}{space 2} .9516143{col 31}{space 2} .3547056{col 42}{space 1}    2.68{col 51}{space 3}0.044{col 59}{space 4} .0398144{col 72}{space 3} 1.863414
{txt}heardkilloutgroup {c |}{col 19}{res}{space 2}-.6299043{col 31}{space 2} .4257867{col 42}{space 1}   -1.48{col 51}{space 3}0.199{col 59}{space 4}-1.724424{col 72}{space 3} .4646151
{txt}{space 2}sawkilloutgroup {c |}{col 19}{res}{space 2} 1.943548{col 31}{space 2} 1.024382{col 42}{space 1}    1.90{col 51}{space 3}0.116{col 59}{space 4}-.6897104{col 72}{space 3} 4.576806
{txt}{space 5}sawkillsunni {c |}{col 19}{res}{space 2}-.3165053{col 31}{space 2} .1805968{col 42}{space 1}   -1.75{col 51}{space 3}0.140{col 59}{space 4}-.7807442{col 72}{space 3} .1477337
{txt}{space 5}punishedisis {c |}{col 19}{res}{space 2} .3211139{col 31}{space 2} .3190665{col 42}{space 1}    1.01{col 51}{space 3}0.360{col 59}{space 4}-.4990726{col 72}{space 3}   1.1413
{txt}{space 7}injuredlib {c |}{col 19}{res}{space 2} .0598969{col 31}{space 2} .4925841{col 42}{space 1}    0.12{col 51}{space 3}0.908{col 59}{space 4}-1.206331{col 72}{space 3} 1.326125
{txt}{space 4}imprisonedlib {c |}{col 19}{res}{space 2}-.3553319{col 31}{space 2} .2720705{col 42}{space 1}   -1.31{col 51}{space 3}0.248{col 59}{space 4}-1.054712{col 72}{space 3} .3440476
{txt}{space 6}dsexassault {c |}{col 19}{res}{space 2} .6226915{col 31}{space 2} .4526103{col 42}{space 1}    1.38{col 51}{space 3}0.227{col 59}{space 4}-.5407804{col 72}{space 3} 1.786163
{txt}{space 2}fampunishedisis {c |}{col 19}{res}{space 2} .3772485{col 31}{space 2} .3079266{col 42}{space 1}    1.23{col 51}{space 3}0.275{col 59}{space 4}-.4143019{col 72}{space 3} 1.168799
{txt}{space 3}faminjuredisis {c |}{col 19}{res}{space 2} .7547815{col 31}{space 2} .1149062{col 42}{space 1}    6.57{col 51}{space 3}0.001{col 59}{space 4} .4594058{col 72}{space 3} 1.050157
{txt}{space 4}faminjuredlib {c |}{col 19}{res}{space 2}-.5629034{col 31}{space 2} .1622265{col 42}{space 1}   -3.47{col 51}{space 3}0.018{col 59}{space 4}-.9799199{col 72}{space 3}-.1458869
{txt}{space 2}womenabusedisis {c |}{col 19}{res}{space 2}-.4640718{col 31}{space 2} .2215525{col 42}{space 1}   -2.09{col 51}{space 3}0.090{col 59}{space 4}-1.033591{col 72}{space 3} .1054469
{txt}{space 5}fleehomeisis {c |}{col 19}{res}{space 2}-.0921195{col 31}{space 2} .1990168{col 42}{space 1}   -0.46{col 51}{space 3}0.663{col 59}{space 4}-.6037085{col 72}{space 3} .4194694
{txt}{space 2}homedamagedisis {c |}{col 19}{res}{space 2}-1.137954{col 31}{space 2} .1463783{col 42}{space 1}   -7.77{col 51}{space 3}0.001{col 59}{space 4}-1.514231{col 72}{space 3}-.7616762
{txt}{space 6}fledhomelib {c |}{col 19}{res}{space 2} .1490063{col 31}{space 2} .4705922{col 42}{space 1}    0.32{col 51}{space 3}0.764{col 59}{space 4}-1.060689{col 72}{space 3} 1.358702
{txt}{space 3}homedamagedlib {c |}{col 19}{res}{space 2}  1.24028{col 31}{space 2} .7149805{col 42}{space 1}    1.73{col 51}{space 3}0.143{col 59}{space 4}-.5976356{col 72}{space 3} 3.078196
{txt}{space 4}homelootedlib {c |}{col 19}{res}{space 2} 1.109981{col 31}{space 2}  .268797{col 42}{space 1}    4.13{col 51}{space 3}0.009{col 59}{space 4} .4190163{col 72}{space 3} 1.800946
{txt}{space 9}iraniraq {c |}{col 19}{res}{space 2}-.5005315{col 31}{space 2} .3109761{col 42}{space 1}   -1.61{col 51}{space 3}0.168{col 59}{space 4}-1.299921{col 72}{space 3}  .298858
{txt}{space 10}gulfwar {c |}{col 19}{res}{space 2} .7794104{col 31}{space 2} .3286727{col 42}{space 1}    2.37{col 51}{space 3}0.064{col 59}{space 4}-.0654696{col 72}{space 3}  1.62429
{txt}{space 6}saddampre90 {c |}{col 19}{res}{space 2} 1.147108{col 31}{space 2} .4284859{col 42}{space 1}    2.68{col 51}{space 3}0.044{col 59}{space 4} .0456502{col 72}{space 3} 2.248566
{txt}{space 5}saddampost90 {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 10}iraqwar {c |}{col 19}{res}{space 2} .0583118{col 31}{space 2} 1.041226{col 42}{space 1}    0.06{col 51}{space 3}0.958{col 59}{space 4}-2.618244{col 72}{space 3} 2.734867
{txt}{space 5}insurgency03 {c |}{col 19}{res}{space 2}  .481462{col 31}{space 2} .3958388{col 42}{space 1}    1.22{col 51}{space 3}0.278{col 59}{space 4}-.5360739{col 72}{space 3} 1.498998
{txt}{space 10}crime03 {c |}{col 19}{res}{space 2}-.0727119{col 31}{space 2} .3551389{col 42}{space 1}   -0.20{col 51}{space 3}0.846{col 59}{space 4}-.9856255{col 72}{space 3} .8402017
{txt}{space 9}hadcovid {c |}{col 19}{res}{space 2}-.5958403{col 31}{space 2} .3863417{col 42}{space 1}   -1.54{col 51}{space 3}0.184{col 59}{space 4}-1.588963{col 72}{space 3} .3972826
{txt}{space 8}diedcovid {c |}{col 19}{res}{space 2} .6473967{col 31}{space 2} .0616664{col 42}{space 1}   10.50{col 51}{space 3}0.000{col 59}{space 4}  .488878{col 72}{space 3} .8059153
{txt}{space 11}female {c |}{col 19}{res}{space 2} .4738951{col 31}{space 2} .3717962{col 42}{space 1}    1.27{col 51}{space 3}0.258{col 59}{space 4}-.4818373{col 72}{space 3} 1.429628
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0147464{col 31}{space 2} .0107938{col 42}{space 1}   -1.37{col 51}{space 3}0.230{col 59}{space 4}-.0424927{col 72}{space 3} .0129999
{txt}{space 5}professional {c |}{col 19}{res}{space 2}-.1509885{col 31}{space 2} .2026838{col 42}{space 1}   -0.74{col 51}{space 3}0.490{col 59}{space 4}-.6720037{col 72}{space 3} .3700268
{txt}{space 10}laborer {c |}{col 19}{res}{space 2}-.4889451{col 31}{space 2} .1808843{col 42}{space 1}   -2.70{col 51}{space 3}0.043{col 59}{space 4}-.9539229{col 72}{space 3}-.0239673
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.5076899{col 31}{space 2}  .312341{col 42}{space 1}   -1.63{col 51}{space 3}0.165{col 59}{space 4}-1.310588{col 72}{space 3} .2952081
{txt}{space 8}westmosul {c |}{col 19}{res}{space 2}-.0336064{col 31}{space 2} .0317085{col 42}{space 1}   -1.06{col 51}{space 3}0.338{col 59}{space 4}-.1151156{col 72}{space 3} .0479028
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  3.31176{col 31}{space 2} .4079733{col 42}{space 1}    8.12{col 51}{space 3}0.000{col 59}{space 4} 2.263032{col 72}{space 3} 4.360489
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. cem sawkillsunni diedcovid if mosul==1, treatment(victimorder)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}4
{txt}Number of matched strata: {res}1

           {txt}  0    1
      All  {res} 82  196
{txt}  Matched  {res} 24  113
{txt}Unmatched  {res} 58   83


{txt}Multivariate L1 distance: {res}0

{txt}Univariate imbalance:

                L1  mean   min   25%   50%   75%   max
sawkillsunni  {res}   0     0     0     0     0     0     0
{txt}   diedcovid  {res}   0     0     0     0     0     0     0
{txt}
{com}. 
. reg closeoutgroup victimorder heardkilloutgroup sawkilloutgroup sawkillsunni punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib iraniraq-crime03 hadcovid diedcovid female age professional laborer unemployed westmosul [pweight=cem_weight], cluster(location)
{txt}(sum of wgt is 137)
{p 0 6 2}note: {bf:sawkillsunni} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:saddampost90} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:diedcovid} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       137
                                                {txt}{help j_robustsingular:F(4, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.5679
                                                {txt}Root MSE          =    {res} 1.1502

{txt}{ralign 83:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    closeoutgroup{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}victimorder {c |}{col 19}{res}{space 2} 1.863525{col 31}{space 2} .4151774{col 42}{space 1}    4.49{col 51}{space 3}0.006{col 59}{space 4} .7962772{col 72}{space 3} 2.930772
{txt}heardkilloutgroup {c |}{col 19}{res}{space 2}-.1605659{col 31}{space 2} .5333424{col 42}{space 1}   -0.30{col 51}{space 3}0.775{col 59}{space 4}-1.531566{col 72}{space 3} 1.210435
{txt}{space 2}sawkilloutgroup {c |}{col 19}{res}{space 2}-.9908775{col 31}{space 2} .3271335{col 42}{space 1}   -3.03{col 51}{space 3}0.029{col 59}{space 4}-1.831801{col 72}{space 3} -.149954
{txt}{space 5}sawkillsunni {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 5}punishedisis {c |}{col 19}{res}{space 2} .2618836{col 31}{space 2} .8491161{col 42}{space 1}    0.31{col 51}{space 3}0.770{col 59}{space 4}-1.920839{col 72}{space 3} 2.444606
{txt}{space 7}injuredlib {c |}{col 19}{res}{space 2}-2.329709{col 31}{space 2} .4942326{col 42}{space 1}   -4.71{col 51}{space 3}0.005{col 59}{space 4}-3.600175{col 72}{space 3}-1.059244
{txt}{space 4}imprisonedlib {c |}{col 19}{res}{space 2}-.5463333{col 31}{space 2} .3540352{col 42}{space 1}   -1.54{col 51}{space 3}0.183{col 59}{space 4} -1.45641{col 72}{space 3} .3637432
{txt}{space 6}dsexassault {c |}{col 19}{res}{space 2}  6.46938{col 31}{space 2}  .910768{col 42}{space 1}    7.10{col 51}{space 3}0.001{col 59}{space 4} 4.128176{col 72}{space 3} 8.810583
{txt}{space 2}fampunishedisis {c |}{col 19}{res}{space 2}-1.150862{col 31}{space 2} .3641822{col 42}{space 1}   -3.16{col 51}{space 3}0.025{col 59}{space 4}-2.087023{col 72}{space 3}-.2147023
{txt}{space 3}faminjuredisis {c |}{col 19}{res}{space 2} .3713302{col 31}{space 2} .3221825{col 42}{space 1}    1.15{col 51}{space 3}0.301{col 59}{space 4}-.4568662{col 72}{space 3} 1.199527
{txt}{space 4}faminjuredlib {c |}{col 19}{res}{space 2}  -1.5342{col 31}{space 2} .5309583{col 42}{space 1}   -2.89{col 51}{space 3}0.034{col 59}{space 4}-2.899072{col 72}{space 3}-.1693285
{txt}{space 2}womenabusedisis {c |}{col 19}{res}{space 2}  .017692{col 31}{space 2} .8257175{col 42}{space 1}    0.02{col 51}{space 3}0.984{col 59}{space 4}-2.104882{col 72}{space 3} 2.140266
{txt}{space 5}fleehomeisis {c |}{col 19}{res}{space 2}-.3479257{col 31}{space 2} .3467747{col 42}{space 1}   -1.00{col 51}{space 3}0.362{col 59}{space 4}-1.239338{col 72}{space 3} .5434869
{txt}{space 2}homedamagedisis {c |}{col 19}{res}{space 2}-3.467303{col 31}{space 2} 1.126833{col 42}{space 1}   -3.08{col 51}{space 3}0.028{col 59}{space 4}-6.363919{col 72}{space 3}-.5706858
{txt}{space 6}fledhomelib {c |}{col 19}{res}{space 2}-.8273458{col 31}{space 2} .1144533{col 42}{space 1}   -7.23{col 51}{space 3}0.001{col 59}{space 4}-1.121557{col 72}{space 3}-.5331342
{txt}{space 3}homedamagedlib {c |}{col 19}{res}{space 2}-.4198296{col 31}{space 2} .6556386{col 42}{space 1}   -0.64{col 51}{space 3}0.550{col 59}{space 4}-2.105202{col 72}{space 3} 1.265543
{txt}{space 4}homelootedlib {c |}{col 19}{res}{space 2} 2.258336{col 31}{space 2} 1.252007{col 42}{space 1}    1.80{col 51}{space 3}0.131{col 59}{space 4}-.9600496{col 72}{space 3} 5.476721
{txt}{space 9}iraniraq {c |}{col 19}{res}{space 2}-1.049984{col 31}{space 2} .2068957{col 42}{space 1}   -5.07{col 51}{space 3}0.004{col 59}{space 4}-1.581826{col 72}{space 3}-.5181414
{txt}{space 10}gulfwar {c |}{col 19}{res}{space 2}-.5382048{col 31}{space 2} .4924825{col 42}{space 1}   -1.09{col 51}{space 3}0.324{col 59}{space 4}-1.804171{col 72}{space 3} .7277617
{txt}{space 6}saddampre90 {c |}{col 19}{res}{space 2} 1.182874{col 31}{space 2} .6037125{col 42}{space 1}    1.96{col 51}{space 3}0.107{col 59}{space 4}-.3690184{col 72}{space 3} 2.734767
{txt}{space 5}saddampost90 {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 10}iraqwar {c |}{col 19}{res}{space 2} .1806084{col 31}{space 2} .6708494{col 42}{space 1}    0.27{col 51}{space 3}0.799{col 59}{space 4}-1.543865{col 72}{space 3} 1.905082
{txt}{space 5}insurgency03 {c |}{col 19}{res}{space 2} -1.11153{col 31}{space 2} .6075384{col 42}{space 1}   -1.83{col 51}{space 3}0.127{col 59}{space 4}-2.673258{col 72}{space 3}  .450197
{txt}{space 10}crime03 {c |}{col 19}{res}{space 2}-1.858059{col 31}{space 2} .6493913{col 42}{space 1}   -2.86{col 51}{space 3}0.035{col 59}{space 4}-3.527372{col 72}{space 3}-.1887457
{txt}{space 9}hadcovid {c |}{col 19}{res}{space 2}-.7469763{col 31}{space 2} .5814824{col 42}{space 1}   -1.28{col 51}{space 3}0.255{col 59}{space 4}-2.241725{col 72}{space 3} .7477719
{txt}{space 8}diedcovid {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 11}female {c |}{col 19}{res}{space 2}-.5316607{col 31}{space 2} .2967252{col 42}{space 1}   -1.79{col 51}{space 3}0.133{col 59}{space 4}-1.294417{col 72}{space 3} .2310957
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0343319{col 31}{space 2} .0097984{col 42}{space 1}   -3.50{col 51}{space 3}0.017{col 59}{space 4}-.0595195{col 72}{space 3}-.0091442
{txt}{space 5}professional {c |}{col 19}{res}{space 2} .4203835{col 31}{space 2} .2744361{col 42}{space 1}    1.53{col 51}{space 3}0.186{col 59}{space 4} -.285077{col 72}{space 3} 1.125844
{txt}{space 10}laborer {c |}{col 19}{res}{space 2} .2061759{col 31}{space 2}  .413303{col 42}{space 1}    0.50{col 51}{space 3}0.639{col 59}{space 4}-.8562534{col 72}{space 3} 1.268605
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}  1.04995{col 31}{space 2} .4335242{col 42}{space 1}    2.42{col 51}{space 3}0.060{col 59}{space 4}-.0644596{col 72}{space 3} 2.164359
{txt}{space 8}westmosul {c |}{col 19}{res}{space 2} .0175972{col 31}{space 2} .0425787{col 42}{space 1}    0.41{col 51}{space 3}0.697{col 59}{space 4}-.0918549{col 72}{space 3} .1270493
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  4.43891{col 31}{space 2} .7199524{col 42}{space 1}    6.17{col 51}{space 3}0.002{col 59}{space 4} 2.588213{col 72}{space 3} 6.289606
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Power Calculations – Experimental Average Treatment Effect
. 
. power twomeans 2.419 3.557 , power(0.80, 0.90, 0.95, 0.99) sd1(1.80) sd2(1.35) nratio(2.39) graph
{res}{txt}
{com}. 
. *Effects of Victimization Priming on Other Out-Group-Related Variables
. 
. *Dictator Game Allocations
. 
. graph bar perdgsunni perdgshia perdgyazidi perdgkurd perdgchristian perdgforeign perdggay if mosul==1,  blabel(bar, format(%9.1f))
{res}{txt}
{com}. *additional formatting required using the file "Dictator Game Allocations.grec" file. 
. 
. catcibar closesunni-closegay if mosul==1
{txt}
{com}. graph save close
{res}{txt}file {bf:close.gph} saved

{com}. catcibar revcontactsunni-revcontactgay if mosul==1
{txt}
{com}. graph save contact
{res}{txt}file {bf:contact.gph} saved

{com}. catcibar revhrsunni-revhrgay if mosul==1
{txt}
{com}. graph save rights
{res}{txt}file {bf:rights.gph} saved

{com}. catcibar revprotectsunni-revprotectgay if mosul==1
{txt}
{com}. graph save protect
{res}{txt}file {bf:protect.gph} saved

{com}. graph combine "close.gph" "contact.gph" "rights.gph" "protect.gph"
{res}{txt}
{com}. *additional formatting required using "Outcomes Bar Graph.grec" file.
. 
. *Code for creating Factor Outcome Variables in READ ME file. (already generated)
. 
. *Effects of Victimization Priming on Out-Group-Related Survey Items (OLS Regression)
. 
. reg factorcloseoutgroup victimorder if mosul==1, robust

{txt}Linear regression                               Number of obs     = {res}       273
                                                {txt}F(1, 271)         =  {res}    26.40
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1028
                                                {txt}Root MSE          =    {res} .82267

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}factorclos~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .6096937{col 26}{space 2} .1186667{col 37}{space 1}    5.14{col 46}{space 3}0.000{col 54}{space 4} .3760678{col 67}{space 3} .8433196
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.4310289{col 26}{space 2} .1049478{col 37}{space 1}   -4.11{col 46}{space 3}0.000{col 54}{space 4}-.6376455{col 67}{space 3}-.2244123
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg factorcontactoutgroup victimorder if mosul==1, robust

{txt}Linear regression                               Number of obs     = {res}       266
                                                {txt}F(1, 264)         =  {res}    69.92
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1884
                                                {txt}Root MSE          =    {res} .82014

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}factorcont~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .8583471{col 26}{space 2} .1026513{col 37}{space 1}    8.36{col 46}{space 3}0.000{col 54}{space 4} .6562276{col 67}{space 3} 1.060467
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6001976{col 26}{space 2}  .081139{col 37}{space 1}   -7.40{col 46}{space 3}0.000{col 54}{space 4}-.7599595{col 67}{space 3}-.4404357
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg factorhroutgroup victimorder if mosul==1, robust

{txt}Linear regression                               Number of obs     = {res}       268
                                                {txt}F(1, 266)         =  {res}     6.67
                                                {txt}Prob > F          = {res}    0.0103
                                                {txt}R-squared         = {res}    0.0425
                                                {txt}Root MSE          =    {res} .97271

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}factorhrou~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .4492255{col 26}{space 2} .1739622{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .1067075{col 67}{space 3} .7917435
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3184808{col 26}{space 2} .1671797{col 37}{space 1}   -1.91{col 46}{space 3}0.058{col 54}{space 4}-.6476447{col 67}{space 3} .0106831
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg factorprotectoutgroup victimorder if mosul==1, robust

{txt}Linear regression                               Number of obs     = {res}       266
                                                {txt}F(1, 264)         =  {res}     3.56
                                                {txt}Prob > F          = {res}    0.0603
                                                {txt}R-squared         = {res}    0.0198
                                                {txt}Root MSE          =    {res} .97256

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}factorprot~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .3024127{col 26}{space 2} .1602985{col 37}{space 1}    1.89{col 46}{space 3}0.060{col 54}{space 4}-.0132136{col 67}{space 3}  .618039
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2137353{col 26}{space 2} .1497984{col 37}{space 1}   -1.43{col 46}{space 3}0.155{col 54}{space 4}-.5086869{col 67}{space 3} .0812163
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Victimization Priming and Out-Group-Related Survey Items (OLS Regression, Extended Controls)
. 
. reg factorcloseoutgroup victimorder heardkilloutgroup sawkilloutgroup sawkillsunni punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib iraniraq-crime03 hadcovid diedcovid female age professional laborer unemployed westmosul if mosul==1, cluster(location)
{txt}{p 0 6 2}note: {bf:saddampost90} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       273
                                                {txt}{help j_robustsingular:F(4, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.3565
                                                {txt}Root MSE          =    {res} .73882

{txt}{ralign 83:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}factorcloseoutg~p{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}victimorder {c |}{col 19}{res}{space 2} .5434021{col 31}{space 2} .1916243{col 42}{space 1}    2.84{col 51}{space 3}0.036{col 59}{space 4} .0508161{col 72}{space 3} 1.035988
{txt}heardkilloutgroup {c |}{col 19}{res}{space 2}-.0882403{col 31}{space 2} .3413937{col 42}{space 1}   -0.26{col 51}{space 3}0.806{col 59}{space 4}-.9658207{col 72}{space 3} .7893401
{txt}{space 2}sawkilloutgroup {c |}{col 19}{res}{space 2} 1.285235{col 31}{space 2} .5921045{col 42}{space 1}    2.17{col 51}{space 3}0.082{col 59}{space 4}-.2368182{col 72}{space 3} 2.807288
{txt}{space 5}sawkillsunni {c |}{col 19}{res}{space 2}-.0891077{col 31}{space 2} .0600566{col 42}{space 1}   -1.48{col 51}{space 3}0.198{col 59}{space 4}-.2434882{col 72}{space 3} .0652727
{txt}{space 5}punishedisis {c |}{col 19}{res}{space 2} .2520852{col 31}{space 2} .1950167{col 42}{space 1}    1.29{col 51}{space 3}0.253{col 59}{space 4}-.2492213{col 72}{space 3} .7533917
{txt}{space 7}injuredlib {c |}{col 19}{res}{space 2}-.0187715{col 31}{space 2} .3015016{col 42}{space 1}   -0.06{col 51}{space 3}0.953{col 59}{space 4}-.7938059{col 72}{space 3}  .756263
{txt}{space 4}imprisonedlib {c |}{col 19}{res}{space 2}-.2372678{col 31}{space 2} .1436246{col 42}{space 1}   -1.65{col 51}{space 3}0.159{col 59}{space 4}-.6064665{col 72}{space 3} .1319309
{txt}{space 6}dsexassault {c |}{col 19}{res}{space 2} .3994231{col 31}{space 2} .1747651{col 42}{space 1}    2.29{col 51}{space 3}0.071{col 59}{space 4}-.0498249{col 72}{space 3}  .848671
{txt}{space 2}fampunishedisis {c |}{col 19}{res}{space 2} .2201473{col 31}{space 2} .2499446{col 42}{space 1}    0.88{col 51}{space 3}0.419{col 59}{space 4}-.4223557{col 72}{space 3} .8626502
{txt}{space 3}faminjuredisis {c |}{col 19}{res}{space 2} .5617463{col 31}{space 2}  .249709{col 42}{space 1}    2.25{col 51}{space 3}0.074{col 59}{space 4} -.080151{col 72}{space 3} 1.203644
{txt}{space 4}faminjuredlib {c |}{col 19}{res}{space 2} -.140826{col 31}{space 2} .1827946{col 42}{space 1}   -0.77{col 51}{space 3}0.476{col 59}{space 4}-.6107145{col 72}{space 3} .3290626
{txt}{space 2}womenabusedisis {c |}{col 19}{res}{space 2}-.2807019{col 31}{space 2} .1632382{col 42}{space 1}   -1.72{col 51}{space 3}0.146{col 59}{space 4}-.7003191{col 72}{space 3} .1389153
{txt}{space 5}fleehomeisis {c |}{col 19}{res}{space 2}  .109113{col 31}{space 2} .1976996{col 42}{space 1}    0.55{col 51}{space 3}0.605{col 59}{space 4}-.3990901{col 72}{space 3} .6173161
{txt}{space 2}homedamagedisis {c |}{col 19}{res}{space 2}-.6448699{col 31}{space 2} .0813005{col 42}{space 1}   -7.93{col 51}{space 3}0.001{col 59}{space 4}-.8538596{col 72}{space 3}-.4358802
{txt}{space 6}fledhomelib {c |}{col 19}{res}{space 2} -.310571{col 31}{space 2} .2470208{col 42}{space 1}   -1.26{col 51}{space 3}0.264{col 59}{space 4}-.9455582{col 72}{space 3} .3244162
{txt}{space 3}homedamagedlib {c |}{col 19}{res}{space 2} .0684507{col 31}{space 2} .2608288{col 42}{space 1}    0.26{col 51}{space 3}0.803{col 59}{space 4} -.602031{col 72}{space 3} .7389323
{txt}{space 4}homelootedlib {c |}{col 19}{res}{space 2} .5825122{col 31}{space 2} .0638748{col 42}{space 1}    9.12{col 51}{space 3}0.000{col 59}{space 4} .4183167{col 72}{space 3} .7467076
{txt}{space 9}iraniraq {c |}{col 19}{res}{space 2}-.4616254{col 31}{space 2} .1804157{col 42}{space 1}   -2.56{col 51}{space 3}0.051{col 59}{space 4}-.9253986{col 72}{space 3} .0021479
{txt}{space 10}gulfwar {c |}{col 19}{res}{space 2} .2434867{col 31}{space 2} .1733944{col 42}{space 1}    1.40{col 51}{space 3}0.219{col 59}{space 4}-.2022378{col 72}{space 3} .6892112
{txt}{space 6}saddampre90 {c |}{col 19}{res}{space 2} .4977609{col 31}{space 2} .1927583{col 42}{space 1}    2.58{col 51}{space 3}0.049{col 59}{space 4} .0022601{col 72}{space 3} .9932618
{txt}{space 5}saddampost90 {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 10}iraqwar {c |}{col 19}{res}{space 2}-.1600578{col 31}{space 2} .6049822{col 42}{space 1}   -0.26{col 51}{space 3}0.802{col 59}{space 4}-1.715214{col 72}{space 3} 1.395098
{txt}{space 5}insurgency03 {c |}{col 19}{res}{space 2} .1879687{col 31}{space 2} .2161717{col 42}{space 1}    0.87{col 51}{space 3}0.424{col 59}{space 4}-.3677182{col 72}{space 3} .7436557
{txt}{space 10}crime03 {c |}{col 19}{res}{space 2}-.0979336{col 31}{space 2} .2171834{col 42}{space 1}   -0.45{col 51}{space 3}0.671{col 59}{space 4}-.6562214{col 72}{space 3} .4603542
{txt}{space 9}hadcovid {c |}{col 19}{res}{space 2}-.2390606{col 31}{space 2} .1841175{col 42}{space 1}   -1.30{col 51}{space 3}0.251{col 59}{space 4}-.7123497{col 72}{space 3} .2342286
{txt}{space 8}diedcovid {c |}{col 19}{res}{space 2} .1959053{col 31}{space 2} .0553438{col 42}{space 1}    3.54{col 51}{space 3}0.017{col 59}{space 4} .0536396{col 72}{space 3}  .338171
{txt}{space 11}female {c |}{col 19}{res}{space 2} .2148462{col 31}{space 2} .2224704{col 42}{space 1}    0.97{col 51}{space 3}0.379{col 59}{space 4}-.3570321{col 72}{space 3} .7867246
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0094371{col 31}{space 2}  .008508{col 42}{space 1}   -1.11{col 51}{space 3}0.318{col 59}{space 4}-.0313077{col 72}{space 3} .0124335
{txt}{space 5}professional {c |}{col 19}{res}{space 2}-.0380603{col 31}{space 2} .1277748{col 42}{space 1}   -0.30{col 51}{space 3}0.778{col 59}{space 4}-.3665159{col 72}{space 3} .2903953
{txt}{space 10}laborer {c |}{col 19}{res}{space 2}-.1124411{col 31}{space 2} .0930356{col 42}{space 1}   -1.21{col 51}{space 3}0.281{col 59}{space 4}-.3515968{col 72}{space 3} .1267146
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.1042328{col 31}{space 2} .1643821{col 42}{space 1}   -0.63{col 51}{space 3}0.554{col 59}{space 4}-.5267905{col 72}{space 3} .3183248
{txt}{space 8}westmosul {c |}{col 19}{res}{space 2}-.0460632{col 31}{space 2}  .036458{col 42}{space 1}   -1.26{col 51}{space 3}0.262{col 59}{space 4}-.1397816{col 72}{space 3} .0476552
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .1050957{col 31}{space 2} .3043483{col 42}{space 1}    0.35{col 51}{space 3}0.744{col 59}{space 4}-.6772566{col 72}{space 3} .8874479
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg factorcontactoutgroup victimorder heardkilloutgroup sawkilloutgroup sawkillsunni punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib iraniraq-crime03 hadcovid diedcovid female age professional laborer unemployed westmosul if mosul==1, cluster(location)
{txt}{p 0 6 2}note: {bf:saddampost90} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       266
                                                {txt}{help j_robustsingular:F(4, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.5376
                                                {txt}Root MSE          =    {res} .65752

{txt}{ralign 83:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}factorcontactou~p{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}victimorder {c |}{col 19}{res}{space 2} .5759339{col 31}{space 2} .1054942{col 42}{space 1}    5.46{col 51}{space 3}0.003{col 59}{space 4} .3047524{col 72}{space 3} .8471153
{txt}heardkilloutgroup {c |}{col 19}{res}{space 2} .3652504{col 31}{space 2} .1926696{col 42}{space 1}    1.90{col 51}{space 3}0.116{col 59}{space 4}-.1300226{col 72}{space 3} .8605234
{txt}{space 2}sawkilloutgroup {c |}{col 19}{res}{space 2}-.0465976{col 31}{space 2} .3454334{col 42}{space 1}   -0.13{col 51}{space 3}0.898{col 59}{space 4}-.9345624{col 72}{space 3} .8413672
{txt}{space 5}sawkillsunni {c |}{col 19}{res}{space 2}-.1178344{col 31}{space 2} .0847468{col 42}{space 1}   -1.39{col 51}{space 3}0.223{col 59}{space 4}-.3356829{col 72}{space 3} .1000141
{txt}{space 5}punishedisis {c |}{col 19}{res}{space 2} .1273624{col 31}{space 2} .2376878{col 42}{space 1}    0.54{col 51}{space 3}0.615{col 59}{space 4}-.4836335{col 72}{space 3} .7383582
{txt}{space 7}injuredlib {c |}{col 19}{res}{space 2}-.1058693{col 31}{space 2} .1399169{col 42}{space 1}   -0.76{col 51}{space 3}0.483{col 59}{space 4}-.4655373{col 72}{space 3} .2537986
{txt}{space 4}imprisonedlib {c |}{col 19}{res}{space 2} .3657642{col 31}{space 2} .1825819{col 42}{space 1}    2.00{col 51}{space 3}0.102{col 59}{space 4}-.1035775{col 72}{space 3} .8351058
{txt}{space 6}dsexassault {c |}{col 19}{res}{space 2} .5969938{col 31}{space 2} .1931256{col 42}{space 1}    3.09{col 51}{space 3}0.027{col 59}{space 4} .1005486{col 72}{space 3} 1.093439
{txt}{space 2}fampunishedisis {c |}{col 19}{res}{space 2} -.032008{col 31}{space 2} .2416502{col 42}{space 1}   -0.13{col 51}{space 3}0.900{col 59}{space 4}-.6531895{col 72}{space 3} .5891736
{txt}{space 3}faminjuredisis {c |}{col 19}{res}{space 2} .1867598{col 31}{space 2} .3987465{col 42}{space 1}    0.47{col 51}{space 3}0.659{col 59}{space 4}-.8382508{col 72}{space 3}  1.21177
{txt}{space 4}faminjuredlib {c |}{col 19}{res}{space 2} .5250499{col 31}{space 2} .1973795{col 42}{space 1}    2.66{col 51}{space 3}0.045{col 59}{space 4} .0176698{col 72}{space 3}  1.03243
{txt}{space 2}womenabusedisis {c |}{col 19}{res}{space 2}-.1978641{col 31}{space 2} .2081936{col 42}{space 1}   -0.95{col 51}{space 3}0.386{col 59}{space 4}-.7330428{col 72}{space 3} .3373147
{txt}{space 5}fleehomeisis {c |}{col 19}{res}{space 2}  .882328{col 31}{space 2} .2128567{col 42}{space 1}    4.15{col 51}{space 3}0.009{col 59}{space 4} .3351625{col 72}{space 3} 1.429493
{txt}{space 2}homedamagedisis {c |}{col 19}{res}{space 2}-.5091796{col 31}{space 2} .3151221{col 42}{space 1}   -1.62{col 51}{space 3}0.167{col 59}{space 4}-1.319227{col 72}{space 3} .3008676
{txt}{space 6}fledhomelib {c |}{col 19}{res}{space 2} .3408426{col 31}{space 2}  .197669{col 42}{space 1}    1.72{col 51}{space 3}0.145{col 59}{space 4}-.1672817{col 72}{space 3} .8489669
{txt}{space 3}homedamagedlib {c |}{col 19}{res}{space 2} .3768993{col 31}{space 2} .2763909{col 42}{space 1}    1.36{col 51}{space 3}0.231{col 59}{space 4} -.333586{col 72}{space 3} 1.087385
{txt}{space 4}homelootedlib {c |}{col 19}{res}{space 2} .5467229{col 31}{space 2} .3082444{col 42}{space 1}    1.77{col 51}{space 3}0.136{col 59}{space 4}-.2456445{col 72}{space 3}  1.33909
{txt}{space 9}iraniraq {c |}{col 19}{res}{space 2}-.2816762{col 31}{space 2} .2006364{col 42}{space 1}   -1.40{col 51}{space 3}0.219{col 59}{space 4}-.7974286{col 72}{space 3} .2340761
{txt}{space 10}gulfwar {c |}{col 19}{res}{space 2}-.6833318{col 31}{space 2} .0810103{col 42}{space 1}   -8.44{col 51}{space 3}0.000{col 59}{space 4}-.8915754{col 72}{space 3}-.4750882
{txt}{space 6}saddampre90 {c |}{col 19}{res}{space 2} .0069927{col 31}{space 2} .1923594{col 42}{space 1}    0.04{col 51}{space 3}0.972{col 59}{space 4} -.487483{col 72}{space 3} .5014683
{txt}{space 5}saddampost90 {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 10}iraqwar {c |}{col 19}{res}{space 2}-.4751674{col 31}{space 2} .1900489{col 42}{space 1}   -2.50{col 51}{space 3}0.054{col 59}{space 4}-.9637037{col 72}{space 3} .0133688
{txt}{space 5}insurgency03 {c |}{col 19}{res}{space 2} .0496754{col 31}{space 2} .1836924{col 42}{space 1}    0.27{col 51}{space 3}0.798{col 59}{space 4} -.422521{col 72}{space 3} .5218717
{txt}{space 10}crime03 {c |}{col 19}{res}{space 2}-.4065779{col 31}{space 2} .1961519{col 42}{space 1}   -2.07{col 51}{space 3}0.093{col 59}{space 4}-.9108023{col 72}{space 3} .0976466
{txt}{space 9}hadcovid {c |}{col 19}{res}{space 2}-.3627779{col 31}{space 2} .1455074{col 42}{space 1}   -2.49{col 51}{space 3}0.055{col 59}{space 4}-.7368167{col 72}{space 3} .0112608
{txt}{space 8}diedcovid {c |}{col 19}{res}{space 2}  .117323{col 31}{space 2} .0584235{col 42}{space 1}    2.01{col 51}{space 3}0.101{col 59}{space 4}-.0328594{col 72}{space 3} .2675054
{txt}{space 11}female {c |}{col 19}{res}{space 2} .0187131{col 31}{space 2}  .097778{col 42}{space 1}    0.19{col 51}{space 3}0.856{col 59}{space 4}-.2326332{col 72}{space 3} .2700593
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0016077{col 31}{space 2} .0029956{col 42}{space 1}    0.54{col 51}{space 3}0.614{col 59}{space 4}-.0060928{col 72}{space 3} .0093082
{txt}{space 5}professional {c |}{col 19}{res}{space 2} .1387148{col 31}{space 2} .0583389{col 42}{space 1}    2.38{col 51}{space 3}0.063{col 59}{space 4}-.0112501{col 72}{space 3} .2886798
{txt}{space 10}laborer {c |}{col 19}{res}{space 2}-.1597718{col 31}{space 2}  .045536{col 42}{space 1}   -3.51{col 51}{space 3}0.017{col 59}{space 4}-.2768259{col 72}{space 3}-.0427177
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2} .0545112{col 31}{space 2}  .070144{col 42}{space 1}    0.78{col 51}{space 3}0.472{col 59}{space 4}-.1257997{col 72}{space 3} .2348221
{txt}{space 8}westmosul {c |}{col 19}{res}{space 2}-.0923434{col 31}{space 2} .0665483{col 42}{space 1}   -1.39{col 51}{space 3}0.224{col 59}{space 4}-.2634113{col 72}{space 3} .0787245
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.3743091{col 31}{space 2}  .243147{col 42}{space 1}   -1.54{col 51}{space 3}0.184{col 59}{space 4}-.9993383{col 72}{space 3}   .25072
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg factorhroutgroup victimorder heardkilloutgroup sawkilloutgroup sawkillsunni punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib iraniraq-crime03 hadcovid diedcovid female age professional laborer unemployed westmosul if mosul==1, cluster(location)
{txt}{p 0 6 2}note: {bf:saddampost90} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       268
                                                {txt}{help j_robustsingular:F(4, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.3598
                                                {txt}Root MSE          =    {res} .84443

{txt}{ralign 83:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1} factorhroutgroup{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}victimorder {c |}{col 19}{res}{space 2} .1532941{col 31}{space 2}  .134975{col 42}{space 1}    1.14{col 51}{space 3}0.308{col 59}{space 4}  -.19367{col 72}{space 3} .5002583
{txt}heardkilloutgroup {c |}{col 19}{res}{space 2}  .168234{col 31}{space 2} .4432703{col 42}{space 1}    0.38{col 51}{space 3}0.720{col 59}{space 4}-.9712284{col 72}{space 3} 1.307697
{txt}{space 2}sawkilloutgroup {c |}{col 19}{res}{space 2}-1.880328{col 31}{space 2} .6312494{col 42}{space 1}   -2.98{col 51}{space 3}0.031{col 59}{space 4}-3.503006{col 72}{space 3}  -.25765
{txt}{space 5}sawkillsunni {c |}{col 19}{res}{space 2}-.7793018{col 31}{space 2} .1179791{col 42}{space 1}   -6.61{col 51}{space 3}0.001{col 59}{space 4}-1.082577{col 72}{space 3} -.476027
{txt}{space 5}punishedisis {c |}{col 19}{res}{space 2} .4048088{col 31}{space 2} .0854627{col 42}{space 1}    4.74{col 51}{space 3}0.005{col 59}{space 4} .1851199{col 72}{space 3} .6244976
{txt}{space 7}injuredlib {c |}{col 19}{res}{space 2}-.5828986{col 31}{space 2}  .562799{col 42}{space 1}   -1.04{col 51}{space 3}0.348{col 59}{space 4} -2.02962{col 72}{space 3} .8638224
{txt}{space 4}imprisonedlib {c |}{col 19}{res}{space 2}-.0559291{col 31}{space 2} .1041796{col 42}{space 1}   -0.54{col 51}{space 3}0.614{col 59}{space 4}-.3237313{col 72}{space 3} .2118731
{txt}{space 6}dsexassault {c |}{col 19}{res}{space 2} .3835818{col 31}{space 2}  .059815{col 42}{space 1}    6.41{col 51}{space 3}0.001{col 59}{space 4} .2298225{col 72}{space 3} .5373411
{txt}{space 2}fampunishedisis {c |}{col 19}{res}{space 2}-.4108282{col 31}{space 2}  .170821{col 42}{space 1}   -2.41{col 51}{space 3}0.061{col 59}{space 4}-.8499376{col 72}{space 3} .0282813
{txt}{space 3}faminjuredisis {c |}{col 19}{res}{space 2}-.1676527{col 31}{space 2} .1863529{col 42}{space 1}   -0.90{col 51}{space 3}0.410{col 59}{space 4}-.6466882{col 72}{space 3} .3113827
{txt}{space 4}faminjuredlib {c |}{col 19}{res}{space 2} .2531798{col 31}{space 2} .1673806{col 42}{space 1}    1.51{col 51}{space 3}0.191{col 59}{space 4}-.1770856{col 72}{space 3} .6834453
{txt}{space 2}womenabusedisis {c |}{col 19}{res}{space 2} .3103314{col 31}{space 2}  .258064{col 42}{space 1}    1.20{col 51}{space 3}0.283{col 59}{space 4}-.3530432{col 72}{space 3} .9737059
{txt}{space 5}fleehomeisis {c |}{col 19}{res}{space 2}-.9373058{col 31}{space 2} .1214378{col 42}{space 1}   -7.72{col 51}{space 3}0.001{col 59}{space 4}-1.249472{col 72}{space 3}  -.62514
{txt}{space 2}homedamagedisis {c |}{col 19}{res}{space 2} .0912042{col 31}{space 2} .2163128{col 42}{space 1}    0.42{col 51}{space 3}0.691{col 59}{space 4}-.4648457{col 72}{space 3} .6472541
{txt}{space 6}fledhomelib {c |}{col 19}{res}{space 2} .2463185{col 31}{space 2} .1697594{col 42}{space 1}    1.45{col 51}{space 3}0.206{col 59}{space 4}-.1900618{col 72}{space 3} .6826988
{txt}{space 3}homedamagedlib {c |}{col 19}{res}{space 2}  .077986{col 31}{space 2} .0841605{col 42}{space 1}    0.93{col 51}{space 3}0.397{col 59}{space 4}-.1383555{col 72}{space 3} .2943274
{txt}{space 4}homelootedlib {c |}{col 19}{res}{space 2} .2990801{col 31}{space 2} .1514717{col 42}{space 1}    1.97{col 51}{space 3}0.105{col 59}{space 4}-.0902904{col 72}{space 3} .6884505
{txt}{space 9}iraniraq {c |}{col 19}{res}{space 2}-.0634895{col 31}{space 2} .0324478{col 42}{space 1}   -1.96{col 51}{space 3}0.108{col 59}{space 4}-.1468994{col 72}{space 3} .0199203
{txt}{space 10}gulfwar {c |}{col 19}{res}{space 2} .5881096{col 31}{space 2} .1462323{col 42}{space 1}    4.02{col 51}{space 3}0.010{col 59}{space 4} .2122074{col 72}{space 3} .9640117
{txt}{space 6}saddampre90 {c |}{col 19}{res}{space 2}-1.247873{col 31}{space 2} .3748299{col 42}{space 1}   -3.33{col 51}{space 3}0.021{col 59}{space 4}-2.211404{col 72}{space 3} -.284342
{txt}{space 5}saddampost90 {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 10}iraqwar {c |}{col 19}{res}{space 2}-.3712949{col 31}{space 2} .1401015{col 42}{space 1}   -2.65{col 51}{space 3}0.045{col 59}{space 4}-.7314373{col 72}{space 3}-.0111526
{txt}{space 5}insurgency03 {c |}{col 19}{res}{space 2}-.2600607{col 31}{space 2} .0597424{col 42}{space 1}   -4.35{col 51}{space 3}0.007{col 59}{space 4}-.4136335{col 72}{space 3}-.1064879
{txt}{space 10}crime03 {c |}{col 19}{res}{space 2}-.3171825{col 31}{space 2} .1997474{col 42}{space 1}   -1.59{col 51}{space 3}0.173{col 59}{space 4}-.8306495{col 72}{space 3} .1962844
{txt}{space 9}hadcovid {c |}{col 19}{res}{space 2}-.2648996{col 31}{space 2} .2247435{col 42}{space 1}   -1.18{col 51}{space 3}0.292{col 59}{space 4}-.8426211{col 72}{space 3}  .312822
{txt}{space 8}diedcovid {c |}{col 19}{res}{space 2}-.3444486{col 31}{space 2} .1866833{col 42}{space 1}   -1.85{col 51}{space 3}0.124{col 59}{space 4}-.8243332{col 72}{space 3} .1354361
{txt}{space 11}female {c |}{col 19}{res}{space 2}-.0884098{col 31}{space 2} .0500175{col 42}{space 1}   -1.77{col 51}{space 3}0.137{col 59}{space 4} -.216984{col 72}{space 3} .0401644
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0013341{col 31}{space 2} .0010289{col 42}{space 1}    1.30{col 51}{space 3}0.251{col 59}{space 4}-.0013108{col 72}{space 3} .0039791
{txt}{space 5}professional {c |}{col 19}{res}{space 2}-.0465457{col 31}{space 2} .0807746{col 42}{space 1}   -0.58{col 51}{space 3}0.589{col 59}{space 4}-.2541834{col 72}{space 3} .1610921
{txt}{space 10}laborer {c |}{col 19}{res}{space 2} .3466023{col 31}{space 2} .1786644{col 42}{space 1}    1.94{col 51}{space 3}0.110{col 59}{space 4}-.1126691{col 72}{space 3} .8058736
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2} .1462575{col 31}{space 2} .0734515{col 42}{space 1}    1.99{col 51}{space 3}0.103{col 59}{space 4}-.0425557{col 72}{space 3} .3350706
{txt}{space 8}westmosul {c |}{col 19}{res}{space 2}  .017319{col 31}{space 2} .0232665{col 42}{space 1}    0.74{col 51}{space 3}0.490{col 59}{space 4}-.0424894{col 72}{space 3} .0771275
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .2681178{col 31}{space 2} .2933783{col 42}{space 1}    0.91{col 51}{space 3}0.403{col 59}{space 4}-.4860353{col 72}{space 3} 1.022271
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg factorprotectoutgroup victimorder heardkilloutgroup sawkilloutgroup sawkillsunni punishedisis injuredlib imprisonedlib dsexassault fampunishedisis faminjuredisis faminjuredlib womenabusedisis fleehomeisis homedamagedisis  fledhomelib homedamagedlib homelootedlib iraniraq-crime03 hadcovid diedcovid female age professional laborer unemployed westmosul if mosul==1, cluster(location)
{txt}{p 0 6 2}note: {bf:saddampost90} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       266
                                                {txt}{help j_robustsingular:F(4, 5) }          =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.2193
                                                {txt}Root MSE          =    {res} .92192

{txt}{ralign 83:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}factorprotectou~p{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}victimorder {c |}{col 19}{res}{space 2} .0824467{col 31}{space 2} .1579653{col 42}{space 1}    0.52{col 51}{space 3}0.624{col 59}{space 4} -.323616{col 72}{space 3} .4885094
{txt}heardkilloutgroup {c |}{col 19}{res}{space 2} .3862122{col 31}{space 2} .2650791{col 42}{space 1}    1.46{col 51}{space 3}0.205{col 59}{space 4}-.2951952{col 72}{space 3}  1.06762
{txt}{space 2}sawkilloutgroup {c |}{col 19}{res}{space 2}-.9455681{col 31}{space 2}   .18791{col 42}{space 1}   -5.03{col 51}{space 3}0.004{col 59}{space 4}-1.428606{col 72}{space 3}-.4625301
{txt}{space 5}sawkillsunni {c |}{col 19}{res}{space 2}-.6113495{col 31}{space 2} .1849655{col 42}{space 1}   -3.31{col 51}{space 3}0.021{col 59}{space 4}-1.086819{col 72}{space 3}-.1358804
{txt}{space 5}punishedisis {c |}{col 19}{res}{space 2} .7354723{col 31}{space 2} .3559278{col 42}{space 1}    2.07{col 51}{space 3}0.094{col 59}{space 4}-.1794693{col 72}{space 3} 1.650414
{txt}{space 7}injuredlib {c |}{col 19}{res}{space 2} .2690881{col 31}{space 2} .3784508{col 42}{space 1}    0.71{col 51}{space 3}0.509{col 59}{space 4}-.7037506{col 72}{space 3} 1.241927
{txt}{space 4}imprisonedlib {c |}{col 19}{res}{space 2} .1881087{col 31}{space 2} .0524305{col 42}{space 1}    3.59{col 51}{space 3}0.016{col 59}{space 4} .0533318{col 72}{space 3} .3228855
{txt}{space 6}dsexassault {c |}{col 19}{res}{space 2}  .729791{col 31}{space 2} .1244506{col 42}{space 1}    5.86{col 51}{space 3}0.002{col 59}{space 4} .4098807{col 72}{space 3} 1.049701
{txt}{space 2}fampunishedisis {c |}{col 19}{res}{space 2} .0541057{col 31}{space 2} .4915976{col 42}{space 1}    0.11{col 51}{space 3}0.917{col 59}{space 4}-1.209586{col 72}{space 3} 1.317797
{txt}{space 3}faminjuredisis {c |}{col 19}{res}{space 2} .0210797{col 31}{space 2} .4932192{col 42}{space 1}    0.04{col 51}{space 3}0.968{col 59}{space 4}-1.246781{col 72}{space 3}  1.28894
{txt}{space 4}faminjuredlib {c |}{col 19}{res}{space 2} .5435039{col 31}{space 2} .2060242{col 42}{space 1}    2.64{col 51}{space 3}0.046{col 59}{space 4}  .013902{col 72}{space 3} 1.073106
{txt}{space 2}womenabusedisis {c |}{col 19}{res}{space 2} 1.091802{col 31}{space 2} .2632303{col 42}{space 1}    4.15{col 51}{space 3}0.009{col 59}{space 4} .4151473{col 72}{space 3} 1.768457
{txt}{space 5}fleehomeisis {c |}{col 19}{res}{space 2} .1726369{col 31}{space 2}  .449983{col 42}{space 1}    0.38{col 51}{space 3}0.717{col 59}{space 4}-.9840813{col 72}{space 3} 1.329355
{txt}{space 2}homedamagedisis {c |}{col 19}{res}{space 2} .8242496{col 31}{space 2} .2959951{col 42}{space 1}    2.78{col 51}{space 3}0.039{col 59}{space 4} .0633701{col 72}{space 3} 1.585129
{txt}{space 6}fledhomelib {c |}{col 19}{res}{space 2} .5151485{col 31}{space 2} .0512319{col 42}{space 1}   10.06{col 51}{space 3}0.000{col 59}{space 4} .3834526{col 72}{space 3} .6468444
{txt}{space 3}homedamagedlib {c |}{col 19}{res}{space 2} .5514525{col 31}{space 2} .0364771{col 42}{space 1}   15.12{col 51}{space 3}0.000{col 59}{space 4} .4576851{col 72}{space 3} .6452199
{txt}{space 4}homelootedlib {c |}{col 19}{res}{space 2} .1500886{col 31}{space 2} .0616939{col 42}{space 1}    2.43{col 51}{space 3}0.059{col 59}{space 4}-.0085006{col 72}{space 3} .3086779
{txt}{space 9}iraniraq {c |}{col 19}{res}{space 2}-.3506726{col 31}{space 2} .1229154{col 42}{space 1}   -2.85{col 51}{space 3}0.036{col 59}{space 4}-.6666366{col 72}{space 3}-.0347087
{txt}{space 10}gulfwar {c |}{col 19}{res}{space 2} .3025987{col 31}{space 2} .2255176{col 42}{space 1}    1.34{col 51}{space 3}0.237{col 59}{space 4}-.2771127{col 72}{space 3} .8823102
{txt}{space 6}saddampre90 {c |}{col 19}{res}{space 2}-1.240211{col 31}{space 2} .5527541{col 42}{space 1}   -2.24{col 51}{space 3}0.075{col 59}{space 4} -2.66111{col 72}{space 3} .1806888
{txt}{space 5}saddampost90 {c |}{col 19}{res}{space 2}        0{col 31}{txt}  (omitted)
{space 10}iraqwar {c |}{col 19}{res}{space 2}-.3505672{col 31}{space 2} .3146797{col 42}{space 1}   -1.11{col 51}{space 3}0.316{col 59}{space 4}-1.159477{col 72}{space 3} .4583428
{txt}{space 5}insurgency03 {c |}{col 19}{res}{space 2}-.2284456{col 31}{space 2} .1269656{col 42}{space 1}   -1.80{col 51}{space 3}0.132{col 59}{space 4}-.5548211{col 72}{space 3} .0979299
{txt}{space 10}crime03 {c |}{col 19}{res}{space 2}-.7472673{col 31}{space 2} .2109408{col 42}{space 1}   -3.54{col 51}{space 3}0.017{col 59}{space 4}-1.289508{col 72}{space 3}-.2050268
{txt}{space 9}hadcovid {c |}{col 19}{res}{space 2}-.1442742{col 31}{space 2} .2795411{col 42}{space 1}   -0.52{col 51}{space 3}0.628{col 59}{space 4}-.8628574{col 72}{space 3}  .574309
{txt}{space 8}diedcovid {c |}{col 19}{res}{space 2}-.0736885{col 31}{space 2} .0956599{col 42}{space 1}   -0.77{col 51}{space 3}0.476{col 59}{space 4}  -.31959{col 72}{space 3}  .172213
{txt}{space 11}female {c |}{col 19}{res}{space 2} .0262751{col 31}{space 2}  .108428{col 42}{space 1}    0.24{col 51}{space 3}0.818{col 59}{space 4}-.2524481{col 72}{space 3} .3049983
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0038838{col 31}{space 2} .0043299{col 42}{space 1}    0.90{col 51}{space 3}0.411{col 59}{space 4}-.0072465{col 72}{space 3} .0150141
{txt}{space 5}professional {c |}{col 19}{res}{space 2} .1846423{col 31}{space 2} .1572047{col 42}{space 1}    1.17{col 51}{space 3}0.293{col 59}{space 4}-.2194654{col 72}{space 3}   .58875
{txt}{space 10}laborer {c |}{col 19}{res}{space 2} .5028953{col 31}{space 2} .2681536{col 42}{space 1}    1.88{col 51}{space 3}0.120{col 59}{space 4}-.1864156{col 72}{space 3} 1.192206
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2} .0126966{col 31}{space 2} .5254875{col 42}{space 1}    0.02{col 51}{space 3}0.982{col 59}{space 4}-1.338112{col 72}{space 3} 1.363505
{txt}{space 8}westmosul {c |}{col 19}{res}{space 2} .0433013{col 31}{space 2} .0551883{col 42}{space 1}    0.78{col 51}{space 3}0.468{col 59}{space 4}-.0985648{col 72}{space 3} .1851674
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.7143458{col 31}{space 2} .0458723{col 42}{space 1}  -15.57{col 51}{space 3}0.000{col 59}{space 4}-.8322643{col 72}{space 3}-.5964272
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Altruism and Out-Group-Related Survey Items (OLS Regression)
. 
. reg factorcloseoutgroup revdgsunnibias if mosul==1 , cluster(location)

{txt}Linear regression                               Number of obs     = {res}       273
                                                {txt}F(1, 5)           =  {res}    16.18
                                                {txt}Prob > F          = {res}    0.0101
                                                {txt}R-squared         = {res}    0.0837
                                                {txt}Root MSE          =    {res} .83141

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}factorcloseo~p{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} .5908454{col 28}{space 2} .1468953{col 39}{space 1}    4.02{col 48}{space 3}0.010{col 56}{space 4} .2132391{col 69}{space 3} .9684518
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.1385132{col 28}{space 2} .1433466{col 39}{space 1}   -0.97{col 48}{space 3}0.378{col 56}{space 4}-.5069974{col 69}{space 3}  .229971
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg factorcontactoutgroup revdgsunnibias if mosul==1 , cluster(location)

{txt}Linear regression                               Number of obs     = {res}       266
                                                {txt}F(1, 5)           =  {res}    24.28
                                                {txt}Prob > F          = {res}    0.0044
                                                {txt}R-squared         = {res}    0.1000
                                                {txt}Root MSE          =    {res} .86366

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}factorcontac~p{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} .6707982{col 28}{space 2} .1361289{col 39}{space 1}    4.93{col 48}{space 3}0.004{col 56}{space 4} .3208677{col 69}{space 3} 1.020729
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} -.161395{col 28}{space 2} .1913777{col 39}{space 1}   -0.84{col 48}{space 3}0.438{col 56}{space 4} -.653347{col 69}{space 3} .3305569
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg factorhroutgroup revdgsunnibias  if mosul==1 , cluster(location)

{txt}Linear regression                               Number of obs     = {res}       268
                                                {txt}F(1, 5)           =  {res}     0.75
                                                {txt}Prob > F          = {res}    0.4271
                                                {txt}R-squared         = {res}    0.0117
                                                {txt}Root MSE          =    {res} .98818

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}factorhroutg~p{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2}-.2517102{col 28}{space 2} .2913721{col 39}{space 1}   -0.86{col 48}{space 3}0.427{col 56}{space 4}-1.000706{col 69}{space 3} .4972855
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0601099{col 28}{space 2} .0896833{col 39}{space 1}    0.67{col 48}{space 3}0.532{col 56}{space 4}-.1704285{col 69}{space 3} .2906483
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg factorprotectoutgroup revdgsunnibias if mosul==1  , cluster(location)

{txt}Linear regression                               Number of obs     = {res}       266
                                                {txt}F(1, 5)           =  {res}    11.57
                                                {txt}Prob > F          = {res}    0.0192
                                                {txt}R-squared         = {res}    0.0135
                                                {txt}Root MSE          =    {res} .97566

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}factorprotec~p{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} .2758163{col 28}{space 2} .0810774{col 39}{space 1}    3.40{col 48}{space 3}0.019{col 56}{space 4} .0674001{col 69}{space 3} .4842325
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0601404{col 28}{space 2} .1055222{col 39}{space 1}   -0.57{col 48}{space 3}0.593{col 56}{space 4}-.3313938{col 69}{space 3}  .211113
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Instrumenting on Victimization Priming (IV-Probit)
. 
. ivregress 2sls factorcloseoutgroup ( revdgsunnibias =  victimorder) if mosul==1, vce(cluster location)
{res}
{txt}{col 1}Instrumental variables 2SLS regression{col 51}Number of obs{col 67}= {res}       273
{txt}{col 1}{col 51}Wald chi2({res}1{txt}){col 67}= {res}     21.57
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}         .
{txt}{col 51}Root MSE{col 67}=    {res} 2.0841

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}factorcloseo~p{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} 5.105028{col 28}{space 2} 1.099191{col 39}{space 1}    4.64{col 48}{space 3}0.000{col 56}{space 4} 2.950653{col 69}{space 3} 7.259402
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-1.196783{col 28}{space 2} .1648786{col 39}{space 1}   -7.26{col 48}{space 3}0.000{col 56}{space 4}-1.519939{col 69}{space 3}-.8736269
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 12 16}Endogenous: {res:revdgsunnibias}{p_end}
{p 0 12 16}Exogenous:{space 1} {res:victimorder}{p_end}

{com}. ivregress 2sls factorcontactoutgroup ( revdgsunnibias =  victimorder) if mosul==1, vce(cluster location)
{res}
{txt}{col 1}Instrumental variables 2SLS regression{col 51}Number of obs{col 67}= {res}       266
{txt}{col 1}{col 51}Wald chi2({res}1{txt}){col 67}= {res}     55.19
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}         .
{txt}{col 51}Root MSE{col 67}=    {res} 2.6864

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}factorcontac~p{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} 6.624588{col 28}{space 2} .8917118{col 39}{space 1}    7.43{col 48}{space 3}0.000{col 56}{space 4} 4.876864{col 69}{space 3} 8.372311
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-1.593886{col 28}{space 2} .1337568{col 39}{space 1}  -11.92{col 48}{space 3}0.000{col 56}{space 4}-1.856044{col 69}{space 3}-1.331727
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 12 16}Endogenous: {res:revdgsunnibias}{p_end}
{p 0 12 16}Exogenous:{space 1} {res:victimorder}{p_end}

{com}. ivregress 2sls factorhroutgroup ( revdgsunnibias =  victimorder) if mosul==1, vce(cluster location)
{res}
{txt}{col 1}Instrumental variables 2SLS regression{col 51}Number of obs{col 67}= {res}       268
{txt}{col 1}{col 51}Wald chi2({res}1{txt}){col 67}= {res}      3.28
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0702
{txt}{col 1}{col 51}R-squared{col 67}= {res}         .
{txt}{col 51}Root MSE{col 67}=    {res} 1.9693

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}factorhroutg~p{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} 3.748605{col 28}{space 2} 2.070373{col 39}{space 1}    1.81{col 48}{space 3}0.070{col 56}{space 4} -.309251{col 69}{space 3} 7.806461
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.8951892{col 28}{space 2} .3185189{col 39}{space 1}   -2.81{col 48}{space 3}0.005{col 56}{space 4}-1.519475{col 69}{space 3}-.2709037
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 12 16}Endogenous: {res:revdgsunnibias}{p_end}
{p 0 12 16}Exogenous:{space 1} {res:victimorder}{p_end}

{com}. ivregress 2sls factorprotectoutgroup ( revdgsunnibias =  victimorder) if mosul==1, vce(cluster location)
{res}
{txt}{col 1}Instrumental variables 2SLS regression{col 51}Number of obs{col 67}= {res}       266
{txt}{col 1}{col 51}Wald chi2({res}1{txt}){col 67}= {res}      3.16
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0753
{txt}{col 1}{col 51}R-squared{col 67}= {res}         .
{txt}{col 51}Root MSE{col 67}=    {res}  1.592

{txt}{ralign 80:(Std. err. adjusted for {res:6} clusters in {res:location})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}factorprotec~p{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdgsunnibias {c |}{col 16}{res}{space 2} 3.329264{col 28}{space 2} 1.871981{col 39}{space 1}    1.78{col 48}{space 3}0.075{col 56}{space 4} -.339752{col 69}{space 3} 6.998279
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.7259297{col 28}{space 2} .2879971{col 39}{space 1}   -2.52{col 48}{space 3}0.012{col 56}{space 4}-1.290394{col 69}{space 3}-.1614657
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 12 16}Endogenous: {res:revdgsunnibias}{p_end}
{p 0 12 16}Exogenous:{space 1} {res:victimorder}{p_end}

{com}. 
. *Victimization Effects – Inclusion of Basra Data
. 
. *Dictator Game Allocations (Mosul-Basra)
. 
. graph bar perdgsunni perdgshia perdgyazidi perdgkurd perdgchristian perdgforeign perdggay if mosul==1,  blabel(bar, format(%9.1f))
{res}{txt}
{com}. *additional formatting required using "Dictator Game Allocation.grec" file.
. 
. *Coding for Mosul-Basra combined Out-group Altruism variable in READ ME file. (already generated)
. *Coding for Mosul-Basra out-group empathy variable in READ ME file.(already generated)
. 
. *Table 2. Effects of Victimization and Empathy on Out-group Altruism (including Basra)
. 
. logit revdgingroupbias mosul sunni c.bcloseoutgroup , cluster(location)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-338.25728}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-165.36527}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-155.47386}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-154.43134}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -154.4202}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -154.4202}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:494}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:135.17}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-154.4202}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.5435}

{txt}{ralign 82:(Std. err. adjusted for {res:12} clusters in {res:location})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}revdgingroupbias{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}mosul {c |}{col 18}{res}{space 2}  -5.5076{col 30}{space 2} .6114469{col 41}{space 1}   -9.01{col 50}{space 3}0.000{col 58}{space 4}-6.706014{col 71}{space 3}-4.309186
{txt}{space 11}sunni {c |}{col 18}{res}{space 2}-1.493691{col 30}{space 2} .8012048{col 41}{space 1}   -1.86{col 50}{space 3}0.062{col 58}{space 4}-3.064023{col 71}{space 3} .0766419
{txt}{space 2}bcloseoutgroup {c |}{col 18}{res}{space 2} .4691602{col 30}{space 2} .0732544{col 41}{space 1}    6.40{col 50}{space 3}0.000{col 58}{space 4} .3255841{col 71}{space 3} .6127362
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 4.162987{col 30}{space 2} .6680737{col 41}{space 1}    6.23{col 50}{space 3}0.000{col 58}{space 4} 2.853587{col 71}{space 3} 5.472388
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgingroupbias mosul sunni c.bcloseoutgroup iraniraq-violence14  , cluster(location)

{txt}note: {bf:saddampost90} != 0 predicts success perfectly;
      {bf:saddampost90} omitted and 16 obs not used.

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-328.91024}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-154.21976}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-142.14564}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-141.20719}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-141.19511}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -141.1951}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:478}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(6)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-141.1951}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.5707}

{txt}{ralign 82:(Std. err. adjusted for {res:12} clusters in {res:location})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}revdgingroupbias{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}mosul {c |}{col 18}{res}{space 2}-5.576018{col 30}{space 2} .7069216{col 41}{space 1}   -7.89{col 50}{space 3}0.000{col 58}{space 4}-6.961558{col 71}{space 3}-4.190477
{txt}{space 11}sunni {c |}{col 18}{res}{space 2}-1.035557{col 30}{space 2} 1.048499{col 41}{space 1}   -0.99{col 50}{space 3}0.323{col 58}{space 4}-3.090577{col 71}{space 3} 1.019464
{txt}{space 2}bcloseoutgroup {c |}{col 18}{res}{space 2} .3780298{col 30}{space 2} .0849197{col 41}{space 1}    4.45{col 50}{space 3}0.000{col 58}{space 4} .2115902{col 71}{space 3} .5444694
{txt}{space 8}iraniraq {c |}{col 18}{res}{space 2}-.1294958{col 30}{space 2} .5189727{col 41}{space 1}   -0.25{col 50}{space 3}0.803{col 58}{space 4}-1.146664{col 71}{space 3} .8876719
{txt}{space 9}gulfwar {c |}{col 18}{res}{space 2} .4688249{col 30}{space 2} 2.109793{col 41}{space 1}    0.22{col 50}{space 3}0.824{col 58}{space 4}-3.666294{col 71}{space 3} 4.603943
{txt}{space 5}saddampre90 {c |}{col 18}{res}{space 2} 3.132016{col 30}{space 2} .4018287{col 41}{space 1}    7.79{col 50}{space 3}0.000{col 58}{space 4} 2.344446{col 71}{space 3} 3.919586
{txt}{space 4}saddampost90 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 9}iraqwar {c |}{col 18}{res}{space 2} 1.370001{col 30}{space 2} .3355918{col 41}{space 1}    4.08{col 50}{space 3}0.000{col 58}{space 4}  .712253{col 71}{space 3} 2.027749
{txt}{space 4}insurgency03 {c |}{col 18}{res}{space 2} 1.021876{col 30}{space 2} .4129443{col 41}{space 1}    2.47{col 50}{space 3}0.013{col 58}{space 4} .2125204{col 71}{space 3} 1.831232
{txt}{space 9}crime03 {c |}{col 18}{res}{space 2}-.2613179{col 30}{space 2} .4335312{col 41}{space 1}   -0.60{col 50}{space 3}0.547{col 58}{space 4}-1.111024{col 71}{space 3} .5883877
{txt}{space 6}violence14 {c |}{col 18}{res}{space 2} .7675575{col 30}{space 2} .4189741{col 41}{space 1}    1.83{col 50}{space 3}0.067{col 58}{space 4}-.0536167{col 71}{space 3} 1.588732
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 3.510262{col 30}{space 2} .6094798{col 41}{space 1}    5.76{col 50}{space 3}0.000{col 58}{space 4} 2.315703{col 71}{space 3}  4.70482
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit revdgingroupbias mosul sunni c.bcloseoutgroup iraniraq-violence14  hadcovid diedcovid female age professional laborer unemployed, cluster(location)

{txt}note: {bf:saddampost90} != 0 predicts success perfectly;
      {bf:saddampost90} omitted and 16 obs not used.

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-328.91024}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-147.23542}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-132.47919}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-130.70195}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-130.69234}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-130.69234}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:478}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(9)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-130.69234}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.6027}

{txt}{ralign 82:(Std. err. adjusted for {res:12} clusters in {res:location})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}revdgingroupbias{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}mosul {c |}{col 18}{res}{space 2}-6.292064{col 30}{space 2} .9283817{col 41}{space 1}   -6.78{col 50}{space 3}0.000{col 58}{space 4}-8.111659{col 71}{space 3} -4.47247
{txt}{space 11}sunni {c |}{col 18}{res}{space 2} -.880325{col 30}{space 2} 1.160996{col 41}{space 1}   -0.76{col 50}{space 3}0.448{col 58}{space 4}-3.155835{col 71}{space 3} 1.395185
{txt}{space 2}bcloseoutgroup {c |}{col 18}{res}{space 2}   .42518{col 30}{space 2} .0546882{col 41}{space 1}    7.77{col 50}{space 3}0.000{col 58}{space 4} .3179932{col 71}{space 3} .5323669
{txt}{space 8}iraniraq {c |}{col 18}{res}{space 2} .1824511{col 30}{space 2} .6062848{col 41}{space 1}    0.30{col 50}{space 3}0.763{col 58}{space 4}-1.005845{col 71}{space 3} 1.370747
{txt}{space 9}gulfwar {c |}{col 18}{res}{space 2} .6727206{col 30}{space 2} 2.270271{col 41}{space 1}    0.30{col 50}{space 3}0.767{col 58}{space 4}-3.776928{col 71}{space 3} 5.122369
{txt}{space 5}saddampre90 {c |}{col 18}{res}{space 2}  3.83882{col 30}{space 2} .6153145{col 41}{space 1}    6.24{col 50}{space 3}0.000{col 58}{space 4} 2.632825{col 71}{space 3} 5.044814
{txt}{space 4}saddampost90 {c |}{col 18}{res}{space 2}        0{col 30}{txt}  (omitted)
{space 9}iraqwar {c |}{col 18}{res}{space 2} 1.693884{col 30}{space 2} .5581944{col 41}{space 1}    3.03{col 50}{space 3}0.002{col 58}{space 4} .5998432{col 71}{space 3} 2.787925
{txt}{space 4}insurgency03 {c |}{col 18}{res}{space 2} 1.735749{col 30}{space 2} .8070167{col 41}{space 1}    2.15{col 50}{space 3}0.031{col 58}{space 4} .1540255{col 71}{space 3} 3.317473
{txt}{space 9}crime03 {c |}{col 18}{res}{space 2} .0070144{col 30}{space 2} .6548536{col 41}{space 1}    0.01{col 50}{space 3}0.991{col 58}{space 4}-1.276475{col 71}{space 3} 1.290504
{txt}{space 6}violence14 {c |}{col 18}{res}{space 2}  1.10146{col 30}{space 2} .7304143{col 41}{space 1}    1.51{col 50}{space 3}0.132{col 58}{space 4}-.3301258{col 71}{space 3} 2.533046
{txt}{space 8}hadcovid {c |}{col 18}{res}{space 2} .2687063{col 30}{space 2}  .423037{col 41}{space 1}    0.64{col 50}{space 3}0.525{col 58}{space 4} -.560431{col 71}{space 3} 1.097844
{txt}{space 7}diedcovid {c |}{col 18}{res}{space 2}-1.150656{col 30}{space 2} .1232402{col 41}{space 1}   -9.34{col 50}{space 3}0.000{col 58}{space 4}-1.392203{col 71}{space 3}  -.90911
{txt}{space 10}female {c |}{col 18}{res}{space 2} 1.268313{col 30}{space 2} .5403428{col 41}{space 1}    2.35{col 50}{space 3}0.019{col 58}{space 4} .2092609{col 71}{space 3} 2.327366
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0011949{col 30}{space 2} .0107089{col 41}{space 1}   -0.11{col 50}{space 3}0.911{col 58}{space 4} -.022184{col 71}{space 3} .0197941
{txt}{space 4}professional {c |}{col 18}{res}{space 2}-.8212125{col 30}{space 2} .2838759{col 41}{space 1}   -2.89{col 50}{space 3}0.004{col 58}{space 4}-1.377599{col 71}{space 3} -.264826
{txt}{space 9}laborer {c |}{col 18}{res}{space 2}-.7126457{col 30}{space 2} .4688836{col 41}{space 1}   -1.52{col 50}{space 3}0.129{col 58}{space 4}-1.631641{col 71}{space 3} .2063493
{txt}{space 6}unemployed {c |}{col 18}{res}{space 2}-.1030773{col 30}{space 2} .2131344{col 41}{space 1}   -0.48{col 50}{space 3}0.629{col 58}{space 4}-.5208131{col 71}{space 3} .3146584
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 4.001923{col 30}{space 2} .8503719{col 41}{space 1}    4.71{col 50}{space 3}0.000{col 58}{space 4} 2.335224{col 71}{space 3} 5.668621
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Yazidi\Dictator Iraq\JCR\JCR Replication Instructions\JCR Legacies Replication log file.do
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 9 Jan 2025, 11:29:19
{txt}{.-}
{smcl}
{txt}{sf}{ul off}